ORCID Profile
0000-0001-7027-067X
Current Organisation
James Cook University
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Publisher: IEEE
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Elsevier BV
Date: 2022
Publisher: JMIR Publications Inc.
Date: 17-12-2021
Abstract: e-presentations to emergency departments (EDs) have been directly associated with increased healthcare cost and length of stay, poorer quality of care and increased morbidity and mortality. Early detection of at-risk patients to EDs can reduce unnecessary re-presentations and provide provision of better quality of care and healthcare planning. Conventional risk predictive models, however, have difficulties when the at-risk patients have erse and complex disease states or demographic profiles. These models also ignore related temporal patient information such as changes in their disease state and personal circumstance which can be used to model the progression of risks. ur aim is to develop a temporal risk predictive model based on recurrent neural network (RNN) can understand temporal relationships between different times of patient presentations to EDs and improve the predictive modelling. e used the data extracted from Health Information Exchange (HIE) system, which included all available ED records from the Nepean hospital in Australia from the period 1 January 2009 to 30 June 2016. A total of 343,014 ED presentations were identified from 170,134 in idual patients. We used the variables including age, marital status, indigenous status, mode of arrival, mode of separations, referred to on departure and diagnosis code which have shown to be correlated to frequent presenters to EDs. We evaluated our RNN model by comparing it to other conventional predictive models using the area under to receiver operating characteristics curve (AUROC). All models were trained using the ED data extracted from the 6 to 12-months period by setting an interval that is ided into an observation window and a prediction window. We further proposed a context-based patient representation learning (CPRL) framework to better characterise the feature representation of patient data and discussed the general extension of our CPRL framework as an optimisation algorithm to improve the feature representation of patient data. sing a 9-month observation with 1-month prediction window (i.e., prediction of at-risk patients of re-presentation to ED in next 1-month), the AUROC for the RNN model was 71.60% compared to AUROCs for logistic regression (57.18%), Naves Bayes (56.35%) and random forest (56.02%). The at-risk patients presented to the ED more frequently (i.e., time (day) differences between presentations become shorter) when their marital status was changed (e.g., from ‘Married’ to ‘Separated’ or ‘Separated’ to ‘Divorced’). These patients also consistently had similar diagnoses during the observation period, indicating that these groups of patients may be the focus of certain integrated cares / interventions to improve the quality of care and reduce the unnecessary re-presentations. ur findings indicate that our RNN improves the predictive modelling, is robust and can effectively understand the disease state and personal circumstance changes within patients over time. We suggest that our model highlights the gaps in ED interventions and can be used to develop tailored integrated cares / interventions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 03-06-2023
DOI: 10.1007/S11633-022-1406-4
Abstract: Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.
Publisher: BMJ
Date: 09-2018
DOI: 10.1136/BMJOPEN-2017-021323
Abstract: To examine the characteristics of frequent visitors (FVs) to emergency departments (EDs) and develop a predictive model to identify those with high risk of a future representations to ED among younger and general population (aged ≤70 years). A retrospective analysis of ED data targeting younger and general patients (aged ≤70 years) were collected between 1 January 2009 and 30 June 2016 from a public hospital in Australia. A total of 343 014 ED presentations were identified from 170 134 in idual patients. Proportion of FVs (those attending four or more times annually), demographic characteristics (age, sex, indigenous and marital status), mode of separation (eg, admitted to ward), triage categories, time of arrival to ED, referral on departure and clinical conditions. Statistical estimates using a mixed-effects model to develop a risk predictive scoring system. The FVs were characterised by young adulthood (32.53%) to late-middle (26.07%) aged patients with a higher proportion of indigenous (5.7%) and mental health-related presentations (10.92%). They were also more likely to arrive by ambulance (36.95%) and leave at own risk without completing their treatments (9.8%). They were also highly associated with socially disadvantage groups such as people who have been orced, widowed or separated (12.81%). These findings were then used for the development of a predictive model to identify potential FVs. The performance of our derived risk predictive model was favourable with an area under the receiver operating characteristic (ie, C-statistic) of 65.7%. The development of a demographic and clinical profile of FVs coupled with the use of predictive model can highlight the gaps in interventions and identify new opportunities for better health outcome and planning.
Publisher: JMIR Publications Inc.
Date: 09-03-2021
DOI: 10.2196/14837
Abstract: Outbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks. This study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making. We developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia. We identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases. We demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases.
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/IJIC.5532
Publisher: IEEE
Date: 04-2017
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: European Respiratory Society (ERS)
Date: 30-01-2017
DOI: 10.1183/13993003.00214-2017
Abstract: This Executive Summary of the Global Strategy for the Diagnosis, Management, and Prevention of COPD (GOLD) 2017 Report focuses primarily on the revised and novel parts of the document. The most significant changes include: 1) the assessment of chronic obstructive pulmonary disease has been refined to separate the spirometric assessment from symptom evaluation. ABCD groups are now proposed to be derived exclusively from patient symptoms and their history of exacerbations 2) for each of the groups A to D, escalation strategies for pharmacological treatments are proposed 3) the concept of de-escalation of therapy is introduced in the treatment assessment scheme 4) nonpharmacologic therapies are comprehensively presented and 5) the importance of comorbid conditions in managing COPD is reviewed.
Publisher: Elsevier BV
Date: 2019
Publisher: IEEE
Date: 06-2020
Publisher: IEEE
Date: 04-2016
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 04-2016
Publisher: Elsevier BV
Date: 09-2023
Publisher: IEEE
Date: 04-2016
Publisher: Springer International Publishing
Date: 2021
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 28-06-2022
Abstract: Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmography-based physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method has recently achieved state-of-the-art performances in learning representative data features by maximising mutual information between different augmented views. However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour/shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. Firstly, we propose a landmark-based spatial augmentation that splits the face into several informative parts based on the Shafer’s dichromatic reflection model to characterise subtle skin colour fluctuations. We also formulate a sparsity-based temporal augmentation exploiting Nyquist–Shannon s ling theorem to effectively capture periodic temporal changes by modelling physiological signal features. Furthermore, we introduce a constrained spatiotemporal loss which generates pseudo-labels for augmented video clips. It is used to regulate the training process and handle complicated noise. We evaluated our framework on 3 public datasets and demonstrated superior performances than other self-supervised methods and achieved competitive accuracy compared to the state-of-the-art supervised methods. Code is available at github.com/Dylan-H-Wang/SLF-RPM.
Publisher: JMIR Publications Inc.
Date: 28-05-2019
Abstract: utbreaks of infectious diseases pose great risks, including hospitalization and death, to public health. Therefore, improving the management of outbreaks is important for preventing widespread infection and mitigating associated risks. Mobile health technology provides new capabilities that can help better capture, monitor, and manage infectious diseases, including the ability to quickly identify potential outbreaks. his study aims to develop a new infectious disease surveillance (IDS) system comprising a mobile app for accurate data capturing and dashboard for better health care planning and decision making. e developed the IDS system using a 2-pronged approach: a literature review on available and similar disease surveillance systems to understand the fundamental requirements and face-to-face interviews to collect specific user requirements from the local public health unit team at the Nepean Hospital, Nepean Blue Mountains Local Health District, New South Wales, Australia. e identified 3 fundamental requirements when designing an electronic IDS system, which are the ability to capture and report outbreak data accurately, completely, and in a timely fashion. We then developed our IDS system based on the workflow, scope, and specific requirements of the public health unit team. We also produced detailed design and requirement guidelines. In our system, the outbreak data are captured and sent from anywhere using a mobile device or a desktop PC (web interface). The data are processed using a client-server architecture and, therefore, can be analyzed in real time. Our dashboard is designed to provide a daily, weekly, monthly, and historical summary of outbreak information, which can be potentially used to develop a future intervention plan. Specific information about certain outbreaks can also be visualized interactively to understand the unique characteristics of emerging infectious diseases. e demonstrated the design and development of our IDS system. We suggest that the use of a mobile app and dashboard will simplify the overall data collection, reporting, and analysis processes, thereby improving the public health responses and providing accurate registration of outbreak information. Accurate data reporting and collection are a major step forward in creating a better intervention plan for future outbreaks of infectious diseases.
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.MEDIA.2019.06.005
Abstract: The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) we extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.
No related grants have been discovered for Euijoon Ahn.