ORCID Profile
0000-0002-0491-4403
Current Organisations
Ontario Tech University
,
University of Technology Sydney
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Expert Systems | Decision Support And Group Support Systems | Information Storage, Retrieval And Management | Image Processing | Computer-Human Interaction | Preventive Medicine | Psychology | Public Health and Health Services | Sensory Processes, Perception And Performance | Biomedical Engineering | Biomechanical Engineering | Computer Perception, Memory And Attention | Information Systems | Medical Physics
ANZSRC Socio-Economic Objective (SEO)
Child health | Behavioural and cognitive sciences | Clinical health not specific to particular organs, diseases and conditions | Preventive medicine | Medical instrumentation | Integrated systems |
Related Links
Publications
Adaption-Based Analytics for Assessment of Human Deconditioning during Deep Space Exploration
Publisher: IEEE
Date: 06-03-2021
Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)
Publisher: JMIR Publications Inc.
Date: 02-08-2019
Abstract: his paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. he aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics iterature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. n total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great ersity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. he current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.
A Framework for the Design of Web Service Based Clinical Management Systems to Support Inter and Intra Organizational Patient Journeys
Publisher: IGI Global
Date: 04-2007
Abstract: The clinical management of premature and ill term babies is challenged by the necessity of several inter and intra organizational patient journeys. Premature and ill-term babies born in regional Australia and Canada must be moved to another hospital with Neonatal Intensive Care Unit (NICU) facilities. NICU babies requiring surgery must be moved to a Level IV NICU for surgery. Current clinical management supports the transfer of limited patient data via paper or telephone exchange. In this article a framework for the design of Web-service-based clinical management systems to support inter and intra organizational patient journeys is presented. A series of Web services are described and integrated and coordinated through BPEL processes enabling greater support for inter- and intra-organizational transfer of patient data. This framework is demonstrated through a NICU case study. A key benefit of this framework is that it enables the establishment of “on demand” patient journeys eliminating the need to establish permanent point-to-point connections.
A system for the transmission, processing and visualisation of EEG to support Irish Neonatal Intensive Care Units
Publisher: IEEE
Date: 04-2012
Heart rate variability as an indicator for morphine pharmacokinetics and pharmacodynamics in critically ill newborn infants
Publisher: IEEE
Date: 08-2014
Real-Time and Retrospective Health-Analytics-as-a-Service: A Novel Framework
Publisher: JMIR Publications Inc.
Date: 18-11-2015
Guest Editors’ Introduction on eHealth and Services Computing in Healthcare
Publisher: MDPI AG
Date: 08-2009
Big data analytics for resilience assessment and development in tactical training serious games
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/CBMS.2016.64
A framework for nursing documentation enabling integration with HER and real-time patient monitoring
Publisher: IEEE
Date: 10-2010
Towards Player Health Analytics in Overwatch
Publisher: IEEE
Date: 08-2020
1618 Identification of Noxious Events for Newborn Infants with a Neural Network
Publisher: BMJ
Date: 10-2012
Detection of apnoea from respiratory time series data using clinically recognizable features and kNN classification
Publisher: IEEE
Date: 07-2013
CBPsp: Complex Business Processes for Stream Processing
Publisher: IEEE
Date: 04-2012
Firefighter Personnel and Their Activities in Extreme Environments
Publisher: Springer International Publishing
Date: 2022
Next generation remote critical care through service-oriented architectures: Challenges and opportunities
Publisher: Springer Science and Business Media LLC
Date: 03-2010
Investigation of adaptation mechanisms during five-day dry immersion utilizing big-data analytics
Publisher: IEEE
Date: 10-2018
A Platform for Real-Time Space Health Analytics as a Service Utilizing Space Data Relays
Publisher: IEEE
Date: 06-03-2021
EBMPracticeNet: A Bilingual National Electronic Point-Of-Care Project for Retrieval of Evidence-Based Clinical Guideline Information and Decision Support
Publisher: JMIR Publications Inc.
Date: 10-07-2013
DOI: 10.2196/RESPROT.2644
Engineering and Information Technology for Paramilitary and Military Tactical Personnel
Publisher: Springer International Publishing
Date: 2022
An Individualized Countermeasure Assessment Framework for Astronauts in Space
Publisher: IEEE
Date: 06-03-2021
A framework for analyzing and measuring business performance with Web services
Publisher: IEEE Comput. Soc
Date: 2003
Heart disease classification through HRV analysis using parallel cascade identification and fast orthogonal search
Publisher: IEEE
Date: 04-2010
Machine learning model for temporal pattern recognition
Publisher: IEEE
Date: 05-2016
The impact of cervical manipulation on heart rate variability.
Publisher: IEEE
Date: 08-2014
Toward a big data healthcare analytics system: A mathematical modeling perspective
Publisher: IEEE
Date: 06-2014
A Cloud Based Big Data Based Online Health Analytics for Rural NICUs and PICUs in India: Opportunities and Challenges
Publisher: IEEE
Date: 06-2017
Fusing dimension reduction and classification for mining interesting frequent patterns in patients data
Publisher: Springer International Publishing
Date: 2018
On the integration of an artifact system and a real-time healthcare analytics system
Publisher: ACM
Date: 11-11-2010
Bush babies broadband: On-demand virtual neonatal intensive care unit support for regional Australia
Publisher: IEEE
Date: 2005
Cardiorespiratory physiological data as an indicator of fentanyl pharmacokinetics and pharmacodynamics in critically ill newborn infants: A case report
Publisher: Elsevier BV
Date: 12-2013
A Delphi Survey of Canadian Respiratory Therapists’ Practice Statements on Pediatric Mechanical Ventilation
Publisher: Daedalus Enterprises
Date: 03-08-2022
Abstract: Pediatric mechanical ventilation practice guidelines are not well established therefore, the European Society for Paediatric and Neonatal Intensive Care (ESPNIC) developed consensus recommendations on pediatric mechanical ventilation management in 2017. However, the guideline's applicability in different health care settings is unknown. This study aimed to determine the consensus on pediatric mechanical ventilation practices from Canadian respiratory therapists' (RTs) perspectives and consensually validate aspects of the ESPNIC guideline. A 3-round modified electronic Delphi survey was conducted contents were guided by ESPNIC. Participants were RTs with at least 5 years of experience working in standalone pediatric ICUs or units with dedicated pediatric intensive care beds across Canada. Round 1 collected open-text feedback, and subsequent rounds gathered feedback using a 6-point Likert scale. Consensus was defined as ≥ 75% agreement if consensus was unmet, statements were revised for re-ranking in the subsequent round. Fifty-two RTs from 14 different pediatric facilities participated in at least one of the 3 rounds. Rounds 1, 2, and 3 had a response rate of 80%, 93%, and 96%, respectively. A total of 59 practice statements achieved consensus by the end of round 3, categorized into 10 sections: (1) noninvasive ventilation and high-flow oxygen therapy, (2) tidal volume and inspiratory pressures, (3) breathing frequency and inspiratory times, (4) PEEP and F This was the first study to survey RTs for their perspectives on the general practice of pediatric mechanical ventilation management in Canada, generally aligning with the ESPNIC guideline. These practice statements considered information from health organizations and institutes, supplemented with clinical remarks. Future studies are necessary to verify and understand these practices' effectiveness.
A method for clinical and physiological event stream processing
Publisher: IEEE
Date: 08-2010
An Alert Notification Subsystem for AI Based Clinical Decision Support: A Protoype in NICU
Publisher: IEEE
Date: 15-12-2021
CoRAD: Visual Analytics for Cohort Analysis
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/ICHI.2016.93
The e-Baby data warehouse: A case study
Publisher: IEEE Comput. Soc
Date: 2002
Implementation of Artifact Detection in Critical Care: A Methodological Review
Publisher: arXiv
Date: 2018
Business process monitoring using web services in B2B e-commerce
Publisher: IEEE
Date: 2002
An Agent-Based System for Trading Partner Management in B2B e-Commerce
Publisher: IEEE Comput. Soc
Date: 2002
Application of TPRMine Method for Identification of Temporal Changes on Patients with COPD: A Case Study in Telehealth
Publisher: IEEE
Date: 07-2020
Women in biomedical engineering and health informatics and its impact on gender representation for accepted publications at IEEE EMBC 2007.
Publisher: IEEE
Date: 08-2008
Antenatal Care in Australia: Process Mapping to Visualise Resources and Care
Publisher: IEEE
Date: 11-2021
Real-time analysis for intensive care: development and deployment of the artemis analytic system.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Integrating Big Data analytics, virtual reality, and ARAIG to support resilience assessment and development in tactical training
Publisher: IEEE
Date: 04-2017
Health Analytics as a Service with Artemis Cloud: Service Availability.
Publisher: IEEE
Date: 07-2020
Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach
Publisher: JMIR Publications Inc.
Date: 09-08-2022
DOI: 10.2196/38428
Abstract: Wait times impact patient satisfaction, treatment effectiveness, and the efficiency of care that the patients receive. Wait time prediction in mental health is a complex task and is affected by the difficulty in predicting the required number of treatment sessions for outpatients, high no-show rates, and the possibility of using group treatment sessions. The task of wait time analysis becomes even more challenging if the input data has low utility, which happens when the data is highly deidentified by removing both direct and quasi identifiers. The first aim of this study was to develop machine learning models to predict the wait time from referral to the first appointment for psychiatric outpatients by using real-time data. The second aim was to enhance the performance of these predictive models by utilizing the system’s knowledge while the input data were highly deidentified. The third aim was to identify the factors that drove long wait times, and the fourth aim was to build these models such that they were practical and easy-to-implement (and therefore, attractive to care providers). We analyzed retrospective highly deidentified administrative data from 8 outpatient clinics at Ontario Shores Centre for Mental Health Sciences in Canada by using 6 machine learning methods to predict the first appointment wait time for new outpatients. We used the system’s knowledge to mitigate the low utility of our data. The data included 4187 patients who received care through 30,342 appointments. The average wait time varied widely between different types of mental health clinics. For more than half of the clinics, the average wait time was longer than 3 months. The number of scheduled appointments and the rate of no-shows varied widely among clinics. Despite these variations, the random forest method provided the minimum root mean square error values for 4 of the 8 clinics, and the second minimum root mean square error for the other 4 clinics. Utilizing the system’s knowledge increased the utility of our highly deidentified data and improved the predictive power of the models. The random forest method, enhanced with the system’s knowledge, provided reliable wait time predictions for new outpatients, regardless of low utility of the highly deidentified input data and the high variation in wait times across different clinics and patient types. The priority system was identified as a factor that contributed to long wait times, and a fast-track system was suggested as a potential solution.
Knowledge Discovery of the Delays Experienced in Reporting COVID-19 Confirmed Positive Cases Using Time to Event Models
Publisher: Springer International Publishing
Date: 2021
A Biological Approach to Building Resilience and Wellness Capacity Among Police Exposed to Posttraumatic Stress Injuries: Protocol for a Randomized Controlled Trial
Publisher: JMIR Publications Inc.
Date: 24-05-2023
DOI: 10.2196/33492
Abstract: Law enforcement officers are routinely exposed to hazardous, disturbing events that can impose severe stress and long-term psychological trauma. As a result, police and other public safety personnel (PSP) are at increased risk of developing posttraumatic stress injuries (PTSIs) and disruptions to the autonomic nervous system (ANS). ANS functioning can be objectively and noninvasively measured by heart rate (HR), heart rate variability (HRV), and respiratory sinus arrhythmia (RSA). Traditional interventions aimed at building resilience among PSP have not adequately addressed the physiological ANS dysregulations that lead to mental and physical health conditions, as well as burnout and fatigue following potential psychological trauma. In this study, we will investigate the efficacy of a web-based Autonomic Modulation Training (AMT) intervention on the following outcomes: (1) reducing self-reported symptoms of PTSI, (2) strengthening ANS physiological resilience and wellness capacity, and (3) exploring how sex and gender are related to baseline differences in psychological and biological PTSI symptoms and response to the AMT intervention. The study is comprised of 2 phases. Phase 1 involves the development of the web-based AMT intervention, which includes 1 session of baseline survey measures, 6 weekly sessions that integrate HRV biofeedback (HRVBF) training with meta-cognitive skill practice, and 1 session of follow-up survey measures. Phase 2 will use a cluster randomized control design to test the effectiveness of AMT on the following prepost outcomes: (1) self-report symptoms of PTSI and other wellness measures (2) physiological indicators of health and resilience including resting HR, HRV, and RSA and (3) the influence of sex and gender on other outcomes. Participants will be recruited for an 8-week study across Canada in rolling cohorts. The study received grant funding in March 2020 and ethics approval in February 2021. Due to delays related to COVID-19, phase 1 was completed in December 2022, and phase 2 pilot testing began in February 2023. Cohorts of 10 participants in the experimental (AMT) and control (prepost assessment only) groups will continue until a total of 250 participants are tested. Data collection from all phases is expected to conclude in December 2025 but may be extended until the intended s le size is reached. Quantitative analyses of psychological and physiological data will be conducted in conjunction with expert coinvestigators. There is an urgent need to provide police and PSP with effective training that improves physical and psychological functioning. Given that help-seeking for PTSI is reduced among these occupational groups, AMT is a promising intervention that can be completed in the privacy of one’s home. Importantly, AMT is a novel program that uniquely addresses the underlying physiological mechanisms that support resilience and wellness promotion and is tailored to the occupational demands of PSP. ClinicalTrials.gov NCT05521360 t2/show/NCT05521360 PRR1-10.2196/33492
Introducing a Conceptual Framework for Architecting Healthcare 4.0 Systems
Publisher: Springer International Publishing
Date: 2021
Privacy Preserving Framework for Big Data Management in Smart Buildings
Publisher: IEEE
Date: 22-03-2021
A Framework for the Design, Development, Testing and Deployment of Reliable Big Data Platforms
Publisher: IEEE
Date: 17-12-2022
Big data in neonatal intensive care
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2013
DOI: 10.1109/MC.2013.157
A framework for the design of web service based clinical management systems to support inter and intra organizational patient journeys
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-60566-050-9.CH033
Abstract: The clinical management of premature and ill term babies is challenged by the necessity of several inter and intra organizational patient journeys. Premature and ill-term babies born in regional Australia and Canada must be moved to another hospital with Neonatal Intensive Care Unit (NICU) facilities. NICU babies requiring surgery must be moved to a Level IV NICU for surgery. Current clinical management supports the transfer of limited patient data via paper or telephone exchange. In this article a framework for the design of Web-service-based clinical management systems to support inter and intra organizational patient journeys is presented. A series of Web services are described and integrated and coordinated through BPEL processes enabling greater support for inter- and intra-organizational transfer of patient data. This framework is demonstrated through a NICU case study. A key benefit of this framework is that it enables the establishment of “on demand” patient journeys eliminating the need to establish permanent point-to-point connections.
A method for modeling health informatics capacity in patient journeys supported by interprofessional teams
Publisher: IEEE
Date: 2011
Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study (Preprint)
Publisher: JMIR Publications Inc.
Date: 13-08-2020
Abstract: linical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments. This adversely affects the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiological data and mitigating the impact of these artifacts. he aim of this study is to evaluate a novel AD framework for integrating AD algorithms with CDSS. We designed the framework with features that support real-time implementation within critical care. In this study, we evaluated the framework and its features in a false alarm reduction study. We developed static framework component models, followed by dynamic framework compositions to formulate four CDSS. We evaluated these formulations using neonatal patient data and validated the six framework features: flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support. e developed four exemplar static AD components with standardized requirements and provisions interfaces that facilitate the interoperability of framework components. These AD components were mixed and matched into four different AD compositions to mitigate the artifacts’ effects. We developed a novel static clinical event detection component that is integrated with each AD composition to formulate and evaluate a dynamic CDSS for peripheral oxygen saturation (SpO sub /sub ) alarm generation. This study collected data from 11 patients with erse pathologies in the neonatal intensive care unit. Collected data streams and corresponding alarms include pulse rate and SpO sub /sub measured from a pulse oximeter (Masimo SET SmartPod) integrated with an Infinity Delta monitor and the heart rate derived from electrocardiography leads attached to a second Infinity Delta monitor. total of 119 SpO sub /sub alarms were evaluated. The lowest achievable SpO sub /sub false alarm rate was 39%, with a sensitivity of 80%. This demonstrates the framework’s utility in identifying the best possible dynamic composition to serve the clinical need for false SpO sub /sub alarm reduction and subsequent alarm fatigue, given the limitations of a small s le size. he framework features, including reusability, signal quality indicator standardization, scalability, and customizability, allow the evaluation and comparison of novel CDSS formulations. The optimal solution for a CDSS can then be hard-coded and integrated within clinical workflows for real-time implementation. The flexibility to serve different clinical needs and standardized component interoperability of the framework supports the potential for a real-time clinical implementation of AD.
A Framework for Online Health Analytics for Advanced Prognostics and Health Management of Astronauts
Publisher: IEEE
Date: 03-2015
A Review of Visual Representations of Physiologic Data
Publisher: JMIR Publications Inc.
Date: 21-11-2016
A platform for real-time online health analytics during spaceflight
Publisher: IEEE
Date: 03-2013
A Temporal Pattern Discovery Method and its Application to Physiological Data: A Case Study (Preprint)
Publisher: JMIR Publications Inc.
Date: 16-12-2019
Abstract: igh frequency data collected from monitors and sensors that provide measures relating to patients’ vital status in intensive care units (NICUs) has the potential to provide valuable insights which can be crucial when making critical decisions for the care of premature and ill term infants. However, this exercise is not trivial when faced with huge volumes of data that are captured every second at the bedside/home. The ability to collect, analyze and understand any hidden relationships in the data that may be vital for clinical decision making is a central challenge. he main goal of this research is to develop a method to detect and represent relationships that may exist in temporal abstractions (TA) and temporal patterns (TP) derived from time oriented data. The premise of this research is that in clinical care, the discovery of unknown relationships among physiological time oriented data can lead to detection of onset of conditions, aid in classifying abnormal or normal behaviors or derive patterns of an altered trajectory towards a problematic future state for a patient. That is, there is great potential to use this approach to uncover previously unknown pathophysiologies that are present in high speed physiological data. his research introduces a TPR process and an associated TPRMine algorithm which adopts a stepwise approach to temporal pattern discovery by first applying a scaled mathematical formulation of the time series data. This is achieved by modelling the problem space as a finite state machine representation where for a given timeframe, a time series data segment transitions from one state to another based on probabilistic weights and then quantifying the many paths a time series data may transition to. he TPRMine Algorithm has been designed, implemented and applied to patient physiological data streams captured from the McMaster Children’s Hospital NICU. The algorithm has been applied to understand the number of states a patient in a NICU bed can transition to in a given time period and a demonstration of formulation of hypothesis tests. In addition, a quantification of these states is completed leading to creation of a vital scoring. With this, it’s possible to understand the percent of time a patient remains in a high or low vital score. he developed method allows understanding the number of states a patient may transition to in any given time period. Adding some clinical context to the identified states facilitates state quantification allowing formulation of thresholds which leads to generating patient scores. This is an approach that can be utilized for identifying patient at risk of some clinical condition prior to disease progress. Additionally the developed method facilitates identification of frequent patterns that could be associated with generated thresholds.
A shareable web service-based intelligent decision support system for on-demand business process management
Publisher: Inderscience Publishers
Date: 2006
Women in biomedical engineering and health informatics
Publisher: IEEE
Date: 08-2007
Extending CRISP-DM to incorporate temporal data mining of multidimensional medical data streams: A neonatal intensive care unit case study
Publisher: IEEE
Date: 08-2009
Classifying neonatal spells using real-time temporal analysis of physiological data streams—verification tests
Publisher: Elsevier BV
Date: 12-2013
XML: Implications and applications
Publisher: IEEE Comput. Soc
Date: 2002
Using XML to facilitate information management across multiple local government agencies
Publisher: IEEE Comput. Soc
Date: 2009
A method for real-time stimulation and response monitoring using big data and its application to tactical training
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/CBMS.2015.44
A Decoupled Data Pipeline and its Reliability Assessment: Case Study in Extreme Climatic Humans Studies
Publisher: IEEE
Date: 15-12-2021
Countermeasure data integration within autonomous space medicine: An extension to artemis in space
Publisher: IEEE
Date: 10-2018
Transfusion in Neonatal Care in relation to Heart Rate Variability (HRV) and Anemia: A Literature Review
Publisher: IEEE
Date: 04-12-2021
Big data analytics for enhanced clinical decision support systems during spaceflight
Publisher: IEEE
Date: 12-2017
The Physiological and Psychological Environment in Humans
Publisher: Springer International Publishing
Date: 2022
Enhance Network Communications in a Cloud-Based Real-Time Health Analytics Platform Using SDN
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/ICHI.2016.69
An architecture for health data collection using off-the-shelf health sensors
Publisher: IEEE
Date: 05-2016
Frameworks and platforms for extreme environments adaptation and resilience monitoring
Publisher: Springer International Publishing
Date: 2022
New Approaches for Integration: Integration of Haptic Garments, Big Data Analytics, and Serious Games for Extreme Environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Space as an Extreme Environment—Galactic Adventures: Exploring the Limits of Human Mind and Body, One Planet at a Time
Publisher: Springer International Publishing
Date: 2022
Implementation of artifact detection in critical care: A methodological review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Space as an Extreme Environment: Technical Considerations
Publisher: Springer International Publishing
Date: 2022
Identification of Temporal Changes on Patients at Risk of LONS with TPRMine: A Case Study in NICU
Publisher: IEEE
Date: 07-2020
Paramilitary and Military Tactical Operations as an Extreme Environment
Publisher: Springer International Publishing
Date: 2022
Big data analytics for continuous assessment of astronaut health risk and its application to human-in-the-loop (HITL) related aerospace
Publisher: American Institute of Aeronautics and Astronautics
Date: 05-01-2017
DOI: 10.2514/6.2017-1096
Temporal abstraction in intelligent clinical data analysis: A survey
Publisher: Elsevier BV
Date: 2007
DOI: 10.1016/J.ARTMED.2006.08.002
Abstract: Intelligent clinical data analysis systems require precise qualitative descriptions of data to enable effective and context sensitive interpretation to take place. Temporal abstraction (TA) provides the means to achieve such descriptions, which can then be used as input to a reasoning engine where they are evaluated against a knowledge base to arrive at possible clinical hypotheses. This paper surveys previous research into the development of intelligent clinical data analysis systems that incorporate TA mechanisms and presents research synergies and trends across the research reviewed, especially those associated with the multi-dimensional nature of real-time patient data streams. The motivation for this survey is case study based research into the development of an intelligent real-time, high-frequency patient monitoring system to provide detection of temporal patterns within multiple patient data streams. The survey was based on factors that are of importance to broaden research into temporal abstraction and on characteristics we believe will assume an increasing level of importance for future clinical IDA systems. These factors were: aspects of the data that is abstracted such as source domain and s le frequency, complexity available within abstracted patterns, dimensionality of the TA and data environment and the knowledge and reasoning underpinning TA processes. It is evident from the review that for intelligent clinical data analysis systems to progress into the future where clinical environments are becoming increasingly data-intensive, the ability for managing multi-dimensional aspects of data at high observation and s le frequencies must be provided. Also, the detection of complex patterns within patient data requires higher levels of TA than are presently available. The conflicting matters of computational tractability and temporal reasoning within a real-time environment present a non-trivial problem for investigation in regard to these matters. Finally, to be able to fully exploit the value of learning new knowledge from stored clinical data through data mining and enable its application to data abstraction, the fusion of data mining and TA processes becomes a necessity.
The Wakefield roundtable discussion on complexity and variability at the bedside
Publisher: Elsevier BV
Date: 09-2010
Big data and artificial intelligence in healthcare: Ethical and social implications of neonatology
Publisher: IEEE
Date: 28-10-2021
State based hidden markov models for temporal pattern discovery in critical care
Publisher: IEEE
Date: 10-2018
Real-time service-oriented architectures to support remote critical care: Trends and challenges
Publisher: IEEE
Date: 2008
Health Informatics for Neonatal Intensive Care Units: An Analytical Modeling Perspective
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Learning to Manage De-escalation Through Simulation: An Exploratory Study
Publisher: Elsevier BV
Date: 04-2023
Design of the ARAIG haptic garment for enhanced resilience assessment and development in tactical training serious games
Publisher: IEEE
Date: 09-2016
Wearable monitors on babies: Big data saving little people
Publisher: IEEE
Date: 06-2013
Real-time online health analytics for interplanetary space missions
Publisher: IEEE
Date: 03-2014
Modelling health informatics capacity for neonatal intensive care patient journeys supported by interprofessional teams
Publisher: Inderscience Publishers
Date: 2013
Mobility in healthcare for remote intensive care unit clinical management
Publisher: IGI Global
Date: 2008
DOI: 10.4018/978-1-60566-050-9.CH054
Abstract: This chapter reviews current research directions in healthcare mobility and assesses its impact on the provision of remote intensive care unit (ICU) clinical management. Intensive care units boast a range of state of the art medical monitoring devices to monitor a patient’s physiological parameters. They also have devices such as ventilators to offer mechanical life support. Computing and IT support within ICUs has focused on monitoring the patients and delivering corresponding alarms to care providers. However many intensive care unit admissions are via intra and inter health care facility transfer, requiring receiving care providers to have access to patient information prior to the patient’s arrival. This indicates that opportunities exist for mobile gadgets, such as personal digital assistants (PDAs) to substantially increase the efficiency and effectiveness of processes surrounding healthcare in the ICUs. The challenge is to transcend the use of these mobile devices beyond the current usage for personal information management and static medical applications also to overcome the challenges of screen size and memory limitations. Finally, the deployment of mobile-enabled solutions within the healthcare domain is hindered by privacy, cost and security considerations and a lack of standards. These are some of the significant topics discussed in this chapter.
Detection of apnoea in newborn infants using impedance respiratory wave data
Publisher: ACTAPRESS
Date: 2011
Smart city architecture for community level services through the internet of things
Publisher: IEEE
Date: 2015
Mobility in Healthcare for Remote Intensive Care Unit Clinical Management
Publisher: IGI Global
Date: 2006
DOI: 10.4018/978-1-59140-817-8.CH006
Abstract: This chapter reviews current research directions in healthcare mobility and assesses its impact on the provision of remote intensive care unit (ICU) clinical management. Intensive care units boast a range of state of the art medical monitoring devices to monitor a patient’s physiological parameters. They also have devices such as ventilators to offer mechanical life support. Computing and IT support within ICUs has focused on monitoring the patients and delivering corresponding alarms to care providers. However many intensive care unit admissions are via intra and inter health care facility transfer, requiring receiving care providers to have access to patient information prior to the patient’s arrival. This indicates that opportunities exist for mobile gadgets, such as personal digital assistants (PDAs) to substantially increase the efficiency and effectiveness of processes surrounding healthcare in the ICUs. The challenge is to transcend the use of these mobile devices beyond the current usage for personal information management and static medical applications also to overcome the challenges of screen size and memory limitations. Finally, the deployment of mobile-enabled solutions within the healthcare domain is hindered by privacy, cost and security considerations and a lack of standards. These are some of the significant topics discussed in this chapter.
A design for modelling the impact of information and communication technologies on patient journeys in neonatal intensive care units
Publisher: Springer Berlin Heidelberg
Date: 2009
A Biological Approach to Building Resilience and Wellness Capacity Among Police Exposed to Posttraumatic Stress Injuries: Protocol for a Randomized Controlled Trial (Preprint)
Publisher: JMIR Publications Inc.
Date: 15-02-2023
Abstract: aw enforcement officers are routinely exposed to hazardous, disturbing events that can impose severe stress and long-term psychological trauma. As a result, police and other public safety personnel (PSP) are at increased risk of developing posttraumatic stress injuries (PTSIs) and disruptions to the autonomic nervous system (ANS). ANS functioning can be objectively and noninvasively measured by heart rate (HR), heart rate variability (HRV), and respiratory sinus arrhythmia (RSA). Traditional interventions aimed at building resilience among PSP have not adequately addressed the physiological ANS dysregulations that lead to mental and physical health conditions, as well as burnout and fatigue following potential psychological trauma. n this study, we will investigate the efficacy of a web-based Autonomic Modulation Training (AMT) intervention on the following outcomes: (1) reducing self-reported symptoms of PTSI, (2) strengthening ANS physiological resilience and wellness capacity, and (3) exploring how sex and gender are related to baseline differences in psychological and biological PTSI symptoms and response to the AMT intervention. he study is comprised of 2 phases. Phase 1 involves the development of the web-based AMT intervention, which includes 1 session of baseline survey measures, 6 weekly sessions that integrate HRV biofeedback (HRVBF) training with meta-cognitive skill practice, and 1 session of follow-up survey measures. Phase 2 will use a cluster randomized control design to test the effectiveness of AMT on the following prepost outcomes: (1) self-report symptoms of PTSI and other wellness measures (2) physiological indicators of health and resilience including resting HR, HRV, and RSA and (3) the influence of sex and gender on other outcomes. Participants will be recruited for an 8-week study across Canada in rolling cohorts. he study received grant funding in March 2020 and ethics approval in February 2021. Due to delays related to COVID-19, phase 1 was completed in December 2022, and phase 2 pilot testing began in February 2023. Cohorts of 10 participants in the experimental (AMT) and control (prepost assessment only) groups will continue until a total of 250 participants are tested. Data collection from all phases is expected to conclude in December 2025 but may be extended until the intended s le size is reached. Quantitative analyses of psychological and physiological data will be conducted in conjunction with expert coinvestigators. here is an urgent need to provide police and PSP with effective training that improves physical and psychological functioning. Given that help-seeking for PTSI is reduced among these occupational groups, AMT is a promising intervention that can be completed in the privacy of one’s home. Importantly, AMT is a novel program that uniquely addresses the underlying physiological mechanisms that support resilience and wellness promotion and is tailored to the occupational demands of PSP. linicalTrials.gov NCT05521360 t2/show/NCT05521360 RR1-10.2196/33492
Enabling the integration of clinical event and physiological data for real-time and retrospective analysis
Publisher: Springer Science and Business Media LLC
Date: 20-01-2015
PHM for astronauts: Elaborating and refining the concept
Publisher: IEEE
Date: 03-2016
That Student Should be a Lion Tamer! StressViz: Designing a Stress Analytics Dashboard for Teachers
Publisher: ACM
Date: 13-03-2023
A Wholistic Approach to Human-in-the-Loop Ecosystem
Publisher: American Institute of Aeronautics and Astronautics
Date: 04-01-2021
DOI: 10.2514/6.2021-0626
Integrating complex business processes for knowledge-driven clinical decision support systems
Publisher: IEEE
Date: 08-2012
Women in biomedical engineering and health informatics.
Publisher: IEEE
Date: 08-2008
A communication tool to improve the patient journey modeling process.
Publisher: IEEE
Date: 08-2006
A Framework for Multidimensional Real-Time Data Analysis: A Case Study for the Detection of Apnoea of Prematurity
Publisher: IGI Global
Date: 2011
Abstract: In this paper, the authors present a framework to support multidimensional analysis of real-time physiological data streams and clinical data. The clinical context for the case study demonstration is neonatal intensive care, focusing specifically on the detection of episodes of central apnoea, a clinically significant problem. The model accounts for the multidimensional and real-time nature of apnoea of prematurity and the associated clinical rules. The framework demonstration includes: 1) defining rules that quantify concurrent behaviours between multiple synchronous data streams and asynchronous data values 2) designing UML models to define present practice event processing for episodes of apnoea 3) translating the model in SPADE to enable the deployment within the real-time processing layer of the Artemis platform, which utilizes IBM’s InfoSphere Streams 4) demonstrating knowledge discovery with simple and complex temporal abstractions of the data streams and 5) presenting results for early detection of episodes of apnoea across multiple physiological data streams.
Trends and opportunities for integrated real time neonatal clinical decision support
Publisher: IEEE
Date: 2012
Multi-dimensional temporal abstraction and data mining of medical time series data: trends and challenges.
Publisher: IEEE
Date: 08-2008
Multi-dimensional knowledge translation: enabling health informatics capacity audits using patient journey models.
Publisher: IEEE
Date: 08-2008
A web services based framework for the transmission of physiological data for local and remote Neonatal Intensive Care
Publisher: IEEE
Date: 2005
DOI: 10.1109/EEE.2005.25
A structured approach to requirements gathering creation using PaJMa models.
Publisher: IEEE
Date: 08-2008
A survey of recent physiological monitoring and transmission to support the service of critical care
Publisher: IEEE
Date: 08-2009
A survey of physiological monitoring data models to support the service of critical care
Publisher: IEEE
Date: 2009
Integration of drug dosing data with physiological data streams using a cloud computing paradigm
Publisher: IEEE
Date: 07-2013
Service oriented architecture for the integration of clinical and physiological data for real-time event stream processing
Publisher: IEEE
Date: 09-2009
A Web-Service based framework for analyzing and measuring business performance
Publisher: Springer Science and Business Media LLC
Date: 04-2004
Automated partial premature infant pain profile scoring using big data analytics
Publisher: IEEE
Date: 12-2017
Remote, real-time monitoring and analysis of vital signs of neonatal graduate infants
Publisher: IEEE
Date: 08-2014
Late onset neonatal sepsis detection in newborn infants via multiple physiological streams
Publisher: Elsevier BV
Date: 02-2013
Standards for physiological data transmission and archiving for the support of the service of critical care
Publisher: Association for Computing Machinery (ACM)
Date: 07-2009
Abstract: Physiological data is monitored and displayed on medical devices around the world every day, and the volume of this data is steadily increasing and newer monitoring devices enter the clinical setting. However, the vast majority of this data is lost since it is most often displayed once as it is recorded, perhaps replayed one or more times while it exists in the device's volatile memory. What little data that is permanently recorded is most commonly saved through hand written annotations, in paper records and in some limited s les stored on hospital clinical information systems. Meanwhile, current methods of data analysis provide opportunities to utilize this data for improved care of these same critical care patients. A major inhibitor to this becoming reality is the lack of standards for the representation, transmission and storage of physiological data. HL7, for ex le, does not include definitions for time series data. Research into the use of these data will soon be reaching the clinical setting and the need for such standards to be defined is becoming urgent.
CRISP-TDM0 for standardized knowledge discovery from physiological data streams: Retinopathy of prematurity and blood oxygen saturation case study
Publisher: IEEE
Date: 12-2017
Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study
Publisher: JMIR Publications Inc.
Date: 27-05-2021
DOI: 10.2196/23495
Abstract: Clinical decision support systems (CDSS) have the potential to lower the patient mortality and morbidity rates. However, signal artifacts present in physiological data affect the reliability and accuracy of the CDSS. Moreover, patient monitors and other medical devices generate false alarms while processing physiological data, further leading to alarm fatigue because of increased noise levels, staff disruption, and staff desensitization in busy critical care environments. This adversely affects the quality of care at the patient bedside. Hence, artifact detection (AD) algorithms play a crucial role in assessing the quality of physiological data and mitigating the impact of these artifacts. The aim of this study is to evaluate a novel AD framework for integrating AD algorithms with CDSS. We designed the framework with features that support real-time implementation within critical care. In this study, we evaluated the framework and its features in a false alarm reduction study. We developed static framework component models, followed by dynamic framework compositions to formulate four CDSS. We evaluated these formulations using neonatal patient data and validated the six framework features: flexibility, reusability, signal quality indicator standardization, scalability, customizability, and real-time implementation support. We developed four exemplar static AD components with standardized requirements and provisions interfaces that facilitate the interoperability of framework components. These AD components were mixed and matched into four different AD compositions to mitigate the artifacts’ effects. We developed a novel static clinical event detection component that is integrated with each AD composition to formulate and evaluate a dynamic CDSS for peripheral oxygen saturation (SpO2) alarm generation. This study collected data from 11 patients with erse pathologies in the neonatal intensive care unit. Collected data streams and corresponding alarms include pulse rate and SpO2 measured from a pulse oximeter (Masimo SET SmartPod) integrated with an Infinity Delta monitor and the heart rate derived from electrocardiography leads attached to a second Infinity Delta monitor. A total of 119 SpO2 alarms were evaluated. The lowest achievable SpO2 false alarm rate was 39%, with a sensitivity of 80%. This demonstrates the framework’s utility in identifying the best possible dynamic composition to serve the clinical need for false SpO2 alarm reduction and subsequent alarm fatigue, given the limitations of a small s le size. The framework features, including reusability, signal quality indicator standardization, scalability, and customizability, allow the evaluation and comparison of novel CDSS formulations. The optimal solution for a CDSS can then be hard-coded and integrated within clinical workflows for real-time implementation. The flexibility to serve different clinical needs and standardized component interoperability of the framework supports the potential for a real-time clinical implementation of AD.
High frequency distributed data stream event correlation to improve neonatal clinical management
Publisher: ACM
Date: 20-06-2007
A comprehensive framework design for continuous quality improvement within the neonatal intensive care unit: Integration of the SPOE, CRISP-DM and PaJMa models
Publisher: IEEE
Date: 06-2014
Synergies of the complexity continuum and the stream computing paradigm
Publisher: Elsevier BV
Date: 06-2011
XML and the semantic web: Implications and applications
Publisher: IEEE
Date: 2003
Proceedings from the Montebello Round Table Discussion. Second annual conference on Complexity and Variability discusses research that brings innovation to the bedside
Publisher: Elsevier BV
Date: 06-2011
Balanced scorecard driven business process definition using XML
Publisher: IEEE
Date: 2003
Classifying neonatal spells using real-time temporal analysis of physiological data streams: Algorithm development
Publisher: IEEE
Date: 2013
An architecture for multi-dimensional temporal abstraction and its application to support neonatal intensive care
Publisher: IEEE
Date: 08-2007
Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID-19.
Publisher: Wiley
Date: 31-03-2022
DOI: 10.1111/JPM.12752
Abstract: In a survey conducted by the World Health Organization (WHO) in the summer of 2020, 93% of countries worldwide acknowledged negative impacts on their mental health services. Previous research during the H1N1 pandemic in 2009 established an increase of patient aggression in psychiatric facilities. Despite expected worsening of mental health, our hospital observed reductions in aggressive behaviour among inpatients and subsequent use of coercive interventions by staff in the months following Covid‐19 pandemic restrictions being implemented. The downward trend in incidents observed during the pandemic has suggested that aggression in mental health hospitals may be more situation‐specific and less so a factor of mental illness. We believe that the reduction in aggressive behaviour observed during the pandemic is related to changes in our organization that occurred in response to concerns about patient well‐being our co‐design approach shifted trust, choice and power. Therefore, practices that support these constructs are needed to maintain the outcomes we experienced. Rather than return to normal in the wake of the pandemic, we are strongly encouraged to sustain the changes we made and continue to find better ways to support and work with the in iduals who rely on or use our services. The global COVID‐19 pandemic has dramatically changed the operation of health care such that many services were put on hold as patients were triaged differently, people delayed seeking care, and transition to virtual care was enacted, including in psychiatric facilities. Most of the media dialogue has been negative however, there have been some silver linings observed. Coinciding with the pandemic has been a reduction in aggressive incidents at our psychiatric hospital, along with the decreased need to use restraints and seclusion to manage behaviour. In this paper, we are taking stock of the changes that have occurred in response to the pandemic in an attempt to share our learnings and offer suggestions so that health care does not necessarily return to “normal”.
The use of heart rate for the assessment of firefighter resilience: A literature review
Publisher: IEEE
Date: 10-2018
An artifact detection framework for clinical decision support systems
Publisher: Springer International Publishing
Date: 2015
Supporting Health Informatics with Platform-as-a-Service Cloud Computing
Publisher: Springer Netherlands
Date: 13-11-2014
Opportunities for a cloud based health analytics as a service for eastern cape initiation schools in South Africa
Publisher: IEEE
Date: 06-2019
A cloud computing framework for real-time rural and remote service of critical care
Publisher: IEEE
Date: 06-2011
A novel framework for event stream processing of clinical practice guidelines
Publisher: IEEE
Date: 2012
Classifying neonatal spells using real-time temporal analysis of physiological data streams—algorithm development
Publisher: Elsevier BV
Date: 02-2013
A method for physiological data transmission and archiving to support the service of critical care using DICOM and HL7.
Publisher: IEEE
Date: 08-2008
On-demand Virtual Neonatal Intensive Care units supporting rural, remote and urban healthcare with Bush Babies Broadband
Publisher: Elsevier BV
Date: 11-2007
Compression of XML physiological data streams to support Neonatal Intensive Care unit web services
Publisher: IEEE
Date: 2005
DOI: 10.1109/EEE.2005.51
A Real-Time Multi-dimensional Visualization Framework for Critical and Complex Environments
Publisher: IEEE
Date: 05-2014
DOI: 10.1109/CBMS.2014.23
A method for a real-time novel premature infant pain profile using high rate, high volume physiological data streams
Publisher: IEEE
Date: 05-2014
DOI: 10.1109/CBMS.2014.29
Service oriented architecture to support real-time implementation of artifact detection in critical care monitoring
Publisher: IEEE
Date: 08-2011
A Social Media Tax Data Warehouse to Manage the Underground Economy
Publisher: IEEE
Date: 06-2019
Adaptive API for Real-Time Streaming Analytics as a Service.
Publisher: IEEE
Date: 07-2019
The impact of gender, medical history and vital status on emergency visits and hospital admissions: A remote patient monitoring case study
Publisher: IEEE
Date: 12-2017
Introduction to the Technical Program of IEEE LifeTech 2020
Publisher: IEEE
Date: 03-2020
Determining levels of arousal using electrocardiography: a study of HRV during transcranial magnetic stimulation.
Publisher: IEEE
Date: 08-2010
Predicting hospital admissions and emergency room visits using remote home monitoring data
Publisher: IEEE
Date: 12-2017
Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care
Publisher: IEEE
Date: 06-2013
Variability analysis with analytics applied to physiological data streams from the neonatal intensive care unit
Publisher: IEEE
Date: 06-2012
Cybernetic Approach to Health Assessment
Publisher: RosNOU
Date: 20-08-2022
DOI: 10.18137/CARDIOMETRY.2022.23.3140
Abstract: The exploration of orbital space served as a prerequisite for the creation of a new direction of medical science in relation to the very extreme conditions of life of spacecraft crews. Space medicine, relying on the most modern research methods and approaches, thanks to the development of new medical devices and the use of unique data analysis algorithms, has made a significant contribution to the development of telemedicine, medical cybernetics, and prenosological principles for assessing the state of human health. The review reflects the main stages in the development of medical cybernetics and prenosological diagnostics based on the assessment of the regulatory components of the cardiovascular system. Discussed the aspects of the application of the method of mathematical analysis of the heart rhythm in relation to the assessment and forecast of the working capacity of cosmonauts, at the simulating model of microgravity and confinement. Shown the useful methodically apply for the healthcare of manufacture teams at the plants, passenger bus driver’s employments. As the part of appliance of the new advance tools of children and adolescents public health during the educating process at schools. The created system for analyzing the current functional state of human health and mathematical models that make it possible to predict its negative changes make it possible to predetermine the vector of development of medicine in the future. The foundations of knowledge gained over the period of more than 70 years of scientific activity of Professor R.M. Bavsky are reflected in promising areas of cardiology research using computer technologies - such as Cardiometry technologies.
A framework to model and translate clinical rules to support complex real-time analysis of physiological and clinical data
Publisher: ACM
Date: 11-11-2010
An Evaluation of Understandability of Patient Journey Models in Mental Health
Publisher: JMIR Publications Inc.
Date: 28-07-2016
Methodologies for implementing clinical applications of complexity at the bedside
Publisher: Elsevier BV
Date: 06-2011
Second IEEE international workshop on real-time service-oriented architecture and applications: RTSOAA 2009
Publisher: IEEE
Date: 2009
A cloud based big data health-analytics-as-a-service framework to support low resource setting neonatal intensive care unit
Publisher: ACM
Date: 14-08-2020
A Flexible Parental Engaged Consent Model for the Secondary Use of Their Infant's Physiological Data in the Neonatal Intensive Care Context
Publisher: IEEE
Date: 08-2017
DOI: 10.1109/ICHI.2017.88
A Rule-Based Temporal Analysis Method for Online Health Analytics and Its Application for Real-Time Detection of Neonatal Spells
Publisher: IEEE
Date: 06-2014
Design of temporal analysis of neonatal vagal spells at different gestational ages using the artemis' framework
Publisher: Elsevier BV
Date: 02-2013
Design of temporal analysis for a novel premature infant pain profile using artemis
Publisher: Elsevier BV
Date: 02-2013
PhysioEx: Visual Analysis of Physiological Event Streams
Publisher: Wiley
Date: 06-2016
DOI: 10.1111/CGF.12909
Automated Sleep-Wake Detection in Neonates from Cerebral Function Monitor Signals
Publisher: IEEE
Date: 05-2014
DOI: 10.1109/CBMS.2014.36
Integrating health information technology into clinical guidelines
Publisher: IEEE
Date: 09-2009
Stream computing and its application to critical care
Publisher: IEEE
Date: 10-2010
Parametric power spectrum analysis of ECG signals for obstructive sleep apnoea classification
Publisher: IEEE
Date: 05-2014
DOI: 10.1109/CBMS.2014.37
Related Organisations
Related Funding Activities
Improving Neonatal Critical Care Through Real Time Analysis
Start Date: 2011
End Date: 2014
Funder: Canadian Institutes of Health Research
View Funded ActivityResilience Assessment And Development For Firefighters Using AI, Big Data And Controlled Climates.
Start Date: 2019
End Date: 2020
Funder: Canadian Institutes of Health Research
View Funded ActivitySymposium On Patient Journey Modelling
Start Date: 2010
End Date: 2010
Funder: Canadian Institutes of Health Research
View Funded ActivityA Dissemination Event Of Best Practices And Lessons Learned In Electronic Health Record Collaboration
Start Date: 2017
End Date: 2018
Funder: Canadian Institutes of Health Research
View Funded ActivityCIHR Team In Neonatal Clinical Decision Support
Start Date: 2011
End Date: 2013
Funder: Canadian Institutes of Health Research
View Funded ActivityCIHR Team In Neonatal Clinical Decision Support
Start Date: 2011
End Date: 2013
Funder: Canadian Institutes of Health Research
View Funded ActivitySymposium On Patient Journey Modelling
Start Date: 2010
End Date: 2010
Funder: Canadian Institutes of Health Research
View Funded ActivityA Dissemination Event Of Best Practices And Lessons Learned In Electronic Health Record Collaboration
Start Date: 2017
End Date: 2018
Funder: Canadian Institutes of Health Research
View Funded ActivityHealth Informatics Laboratory
Start Date: 2007
End Date: 2017
Funder: Canada Foundation for Innovation
View Funded ActivityHealth Informatics Laboratory
Start Date: 2007
End Date: 2014
Funder: Canada Foundation for Innovation
View Funded ActivityCombining Novel Datasets For Understanding Illness Trajectory: The CONDUIT Database Project
Start Date: 2013
End Date: 2014
Funder: Canadian Institutes of Health Research
View Funded ActivityCRC Tier 2
Start Date: 2012
End Date: 2017
Funder: Canadian Institutes of Health Research
View Funded ActivityImproving Neonatal Critical Care Through Real Time Analysis
Start Date: 2011
End Date: 2014
Funder: Canadian Institutes of Health Research
View Funded ActivityPerception And Action In Auditory Scenes (PAAS): Neural, Behavioural, Computational And Mechanical Systems
Start Date: 2003
End Date: 2003
Funder: Australian Research Council
View Funded ActivityCRC Tier 2
Start Date: 2012
End Date: 2017
Funder: Canadian Institutes of Health Research
View Funded ActivityCombining Novel Datasets For Understanding Illness Trajectory: The CONDUIT Database Project
Start Date: 2013
End Date: 2014
Funder: Canadian Institutes of Health Research
View Funded ActivityHealth Informatics Laboratory
Start Date: 2007
End Date: 2012
Funder: Ministry of Research, Innovation and Science
View Funded ActivityCRC Tier 2
Start Date: 2012
End Date: 2017
Funder: Canadian Institutes of Health Research
View Funded ActivityCRC Tier 2
Start Date: 2012
End Date: 2017
Funder: Canadian Institutes of Health Research
View Funded ActivityCombining Novel Datasets For Understanding Illness Trajectory: The CONDUIT Database Project
Start Date: 2013
End Date: 2014
Funder: Canadian Institutes of Health Research
View Funded ActivityCIHR Team In Neonatal Clinical Decision Support
Start Date: 2011
End Date: 2013
Funder: Canadian Institutes of Health Research
View Funded ActivitySymposium On Patient Journey Modelling
Start Date: 2010
End Date: 2010
Funder: Canadian Institutes of Health Research
View Funded ActivityHealth Informatics Laboratory
Start Date: 2007
End Date: 2017
Funder: Canada Foundation for Innovation
View Funded ActivityImproving Neonatal Critical Care Through Real Time Analysis
Start Date: 2011
End Date: 2014
Funder: Canadian Institutes of Health Research
View Funded ActivityThe Australian Research Network For Medical Devices: Advanced Technology Solutions For Patients And Practitioners
Start Date: 2003
End Date: 2003
Funder: Australian Research Council
View Funded Activity1+1- A Healthy Start To Life:Targeting The Year Before And The Year After Birth In Aboriginal Children In Remote Areas
Start Date: 2007
End Date: 2012
Funder: National Health and Medical Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP0772335
Start Date: 2007
End Date: 09-2009
Amount: $87,384.00
Funder: Australian Research Council
View Funded ActivitySpecial Research Initiatives - Grant ID: SR0354734
Start Date: 2004
End Date: 12-2004
Amount: $10,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP0349279
Start Date: 10-2003
End Date: 03-2007
Amount: $73,099.00
Funder: Australian Research Council
View Funded ActivitySpecial Research Initiatives - Grant ID: SR0354596
Start Date: 01-2004
End Date: 11-2004
Amount: $20,000.00
Funder: Australian Research Council
View Funded Activity