ORCID Profile
0000-0003-0241-5376
Current Organisation
UNSW Sydney
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Publisher: SAGE Publications
Date: 29-08-2020
Abstract: To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions—such as digital scribes—must focus on identifying the 20% relevant information for automatically generating consultation summaries.
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-3998-0.CH010
Abstract: The role of collaboration in the realm of social creativity has been the focus of cutting edge research in design studies. In this paper, the authors investigate the role of collaboration in the process of creative design and propose a computational model of creativity based on the newly proposed meta-design approach. Meta-design is a unique participatory approach to design that deals with opening up of design solution spaces, and is aimed at creating a viable social platform for collaborative design. A meta-design-based collaborative approach to the design process may achieve ET-creativity by expanding the conceptual space of design beyond what would have been possible by in idual, non-collaborative design. The model has been implemented using interactive genetic algorithms, which casts the design problem as an optimization problem and uses a set of collaborative users for subjective fitness evaluation. The design problems investigated include the collaborative design of architectural floorplans and editorial design of brochures.
Publisher: Elsevier BV
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 14-08-2020
DOI: 10.1101/2020.08.12.20173872
Abstract: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Clinical data—demographics, signs, symptoms, comorbidities and blood test results—and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical and imaging data by testing multiple machine learning models, and further explored the use of four overs ling methods to address the imbalance distribution issue. Features with the highest predictive power were identified using the SHAP framework. Targeting differentiation between mild and severe cases, logistic regression models achieved the best performance on clinical features (AUC:0.848, sensitivity:0.455, specificity:0.906), imaging features (AUC:0.926, sensitivity:0.818, specificity:0.901) and the combined features (AUC:0.950, sensitivity:0.764, specificity:0.919). The SMOTE overs ling method further improved the performance of the combined features to AUC of 0.960 (sensitivity:0.845, specificity:0.929). Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with findings from previous studies. Overs ling yielded mixed results, although it achieved the best performance in our study. This study indicates that clinical and imaging features can be used for automated severity assessment of COVID-19 patients and have the potential to assist with triaging COVID-19 patients and prioritizing care for patients at higher risk of severe cases.
Publisher: Elsevier BV
Date: 03-2022
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: ACM
Date: 28-04-2007
Publisher: JMIR Publications Inc.
Date: 12-09-2018
Abstract: echnological interventions such as mobile apps, Web-based social networks, and wearable trackers have the potential to influence physical activity yet, only a few studies have examined the efficacy of an intervention bundle combining these different technologies. his study aimed to pilot test an intervention composed of a social networking mobile app, connected with a wearable tracker, and investigate its efficacy in improving physical activity, as well as explore participant engagement and the usability of the app. his was a pre-post quasi-experimental study with 1 arm, where participants were subjected to the intervention for a 6-month period. The primary outcome measure was the difference in daily step count between baseline and 6 months. Secondary outcome measures included engagement with the intervention and system usability. Descriptive and inferential statistical tests were conducted posthoc subgroup analyses were carried out for participants with different levels of steps at baseline, app usage, and social features usage. total of 55 participants were enrolled in the study the mean age was 23.6 years and 28 (51%) were female. There was a nonstatistically significant increase in the average daily step count between baseline and 6 months (mean change=14.5 steps/day, P=.98, 95% CI –1136.5 to 1107.5). Subgroup analysis comparing the higher and lower physical activity groups at baseline showed that the latter had a statistically significantly higher increase in their daily step count (group difference in mean change from baseline to 6 months=3025 steps per day, P=.008, 95% CI 837.9-5211.8). At 6 months, the retention rate was 82% (45/55) app usage decreased over time. The mean system usability score was 60.1 (SD 19.2). his study showed the preliminary efficacy of a mobile social networking intervention, integrated with a wearable tracker to promote physical activity, particularly for less physically active subgroups of the population. Future research should explore how to address challenges faced by physically inactive people to provide tailored advices. In addition, users’ perspectives should be explored to shed light on factors that might influence their engagement with the intervention.
Publisher: JMIR Publications Inc.
Date: 08-08-2018
DOI: 10.2196/10153
Abstract: Research in psychology has shown that the way a person walks reflects that person’s current mood (or emotional state). Recent studies have used mobile phones to detect emotional states from movement data. The objective of our study was to investigate the use of movement sensor data from a smart watch to infer an in idual’s emotional state. We present our findings of a user study with 50 participants. The experimental design is a mixed-design study: within-subjects (emotions: happy, sad, and neutral) and between-subjects (stimulus type: audiovisual “movie clips” and audio “music clips”). Each participant experienced both emotions in a single stimulus type. All participants walked 250 m while wearing a smart watch on one wrist and a heart rate monitor strap on the chest. They also had to answer a short questionnaire (20 items Positive Affect and Negative Affect Schedule, PANAS) before and after experiencing each emotion. The data obtained from the heart rate monitor served as supplementary information to our data. We performed time series analysis on data from the smart watch and a t test on questionnaire items to measure the change in emotional state. Heart rate data was analyzed using one-way analysis of variance. We extracted features from the time series using sliding windows and used features to train and validate classifiers that determined an in idual’s emotion. Overall, 50 young adults participated in our study of them, 49 were included for the affective PANAS questionnaire and 44 for the feature extraction and building of personal models. Participants reported feeling less negative affect after watching sad videos or after listening to sad music, P .006. For the task of emotion recognition using classifiers, our results showed that personal models outperformed personal baselines and achieved median accuracies higher than 78% for all conditions of the design study for binary classification of happiness versus sadness. Our findings show that we are able to detect changes in the emotional state as well as in behavioral responses with data obtained from the smartwatch. Together with high accuracies achieved across all users for classification of happy versus sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.
Publisher: JMIR Publications Inc.
Date: 22-05-2020
Abstract: ffective behavior change interventions may require ongoing personalized support for users. Rapid developments in digital technology and artificial intelligence are giving rise to more advanced types of personalized interventions that can analyze large amounts of data to provide real-time, contextualized support. Despite growing research attention, there is still a lack of consensus in the literature about what is considered a personalized system, and how to design such system. This paper provides a definition of personalization and proposes a set of building blocks to design and implement personalized behavior change interventions, drawing on concepts from control systems engineering. We also discuss existing challenges in evaluating the net effects of personalized interventions and outline future directions in this field.
Publisher: BMJ
Date: 21-12-2021
DOI: 10.1136/BJSPORTS-2020-102892
Abstract: To determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback. Systematic review and meta-analysis. PubMed and seven additional databases, from 2007 to 2020. Randomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted. We conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses. Physical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents. Thirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I 2 =69%, T 2 =0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression. Interventions using apps or trackers seem to be effective in promoting physical activity. Longer studies are needed to assess the impact of different intervention components on long-term engagement and effectiveness.
Publisher: Oxford University Press (OUP)
Date: 26-08-2020
Abstract: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.
Publisher: JMIR Publications Inc.
Date: 07-11-2019
DOI: 10.2196/15360
Abstract: The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals (2) involved a conversational agent with an unconstrained natural language interface (3) tested the system with human subjects and (4) implemented personalization features. The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common ex les of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Most of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.
Publisher: Springer Netherlands
Date: 2008
Publisher: MDPI AG
Date: 15-11-2021
DOI: 10.3390/INFO12110471
Abstract: Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from in idual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 05-2009
Publisher: JMIR Publications Inc.
Date: 04-07-2019
Abstract: he personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. he goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. e searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals (2) involved a conversational agent with an unconstrained natural language interface (3) tested the system with human subjects and (4) implemented personalization features. he search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common ex les of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. ost of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.
Publisher: JMIR Publications Inc.
Date: 15-02-2018
Abstract: esearch in psychology has shown that the way a person walks reflects that person's current mood (or emotional state). Recent studies have started using smartphones to detect emotional states from movement data. his study investigates the use of movement sensor data from a smart watch to infer an in idual's emotional state. We present our findings on a user study with 50 participants. he experimental design is a mixed-design study within-subjects (emotions happy, sad, neutral) and between-subjects (stimulus type: audio visual "movie clips", audio "music clips"). Each participant experienced both emotions in a single stimulus type. All participants walked 250m while wearing a smart watch on one wrist and a heart rate monitor strap on their chest. They also had to answer a short questionnaire (20 items PANAS) before and after experiencing each emotion. The heart rate monitor serves as a supplementary information to our data. We performed time-series analysis on the data from the smart watch and a t-test on the questionnaire items to measure the change in emotional state. The heart rate data was analyzed using one-way ANOVA. We extracted features from the time-series using sliding windows and used the features to train and validate classifiers that determine an in idual's emotion. e had 50 young adults participate in our study, with 49 included for the affective PANAS questionnaire and all for the feature extraction. Participants reported feeling less negative affect after watching sad videos or after listening to the sad music, P .006. For the task of emotion recognition using classifiers, our results show that the personal models outperformed personal baselines, and achieve median accuracies higher than 78% for all conditions of the design study for the binary classification of happiness vs sadness. ur findings show that we are able to detect the changes in emotional state with data obtained from the smartwatch as well as behavioral responses. Together with the high accuracies achieved across all users for the classification of happy vs sad emotional states, this is further evidence for the hypothesis that movement sensor data can be used for emotion recognition.
Publisher: ASME International
Date: 19-08-2016
DOI: 10.1115/1.4033542
Abstract: The use of classification techniques for machine health monitoring and fault diagnosis has been popular in recent years. System response in the form of time series data can be used to identify the type of defect and severity of defect. However, a central issue with time series classification is that of identifying appropriate features for classification. In this paper, we explore a new feature set based on delay differential equations (DDEs). DDEs have been used recently for extracting features for classification but have never been used to classify system responses. The Duffing oscillator, Van der Pol–Duffing (VDP-D) oscillator, Lu oscillator, and Chen oscillator are used as ex les for dynamic systems, and the responses are classified into self-similar groups. Responses with the same period should belong to the same group. Misclassification rate is used as an indicator of the efficacy of the feature set. The proposed feature set is compared to a statistical feature set, a power spectral coefficient feature set, and a wavelet coefficient feature set. In the work described in this paper, a density-estimation algorithm called DBSCAN is used as the classification algorithm. The proposed DDE-based feature set is found to be significantly better than the other feature sets for classifying responses generated by the Duffing, Lu, and Chen systems. The wavelet and power spectral coefficient data sets are not found to be significantly better than the statistical feature set for these systems. None of the feature sets tested is discerning enough on the VDP-D system.
Publisher: ACM
Date: 10-09-2020
Publisher: JMIR Publications Inc.
Date: 28-03-2019
DOI: 10.2196/12181
Publisher: Cold Spring Harbor Laboratory
Date: 07-09-2021
DOI: 10.1101/2021.09.03.21263104
Abstract: Objective: To investigate clinical and health system factors associated with receiving catheter ablation (CA) for non-valvular atrial fibrillation (AF). Study Design and Setting: We used hospital administrative data linked with death registrations in New South Wales, Australia for patients with a primary diagnosis of AF between 2009 and 2017. We investigated factors associated with receiving CA (using Cox regression) and early ablation (using logistic regression). Results: Cardioversion during index admission (hazard ratio [HR] 1.96 95% CI 1.75-2.19), year of index admission (HR 1.07 1.07 95% CI 1.05-1.10), private patient status (HR 2.65 95% CI 2.35-2.97), and living in more advantaged areas (HR 1.18 95% CI 1.13-1.22) were associated with a higher likelihood of receiving CA. Private patient status (odds ratio [OR] 2.04 95% CI 1.59-2.61) and a history of cardioversion (OR 1.25 95% CI 1.0-1.57) and diabetes (OR 1.6 95% CI 1.06-2.41) were associated with receiving early ablation. Conclusion: Beyond clinical factors, private patients are more likely to receive CA and earlier ablation than their public counterparts. Whether the earlier access to ablation procedures in private patients is leading to differences in outcomes among patients with atrial fibrillation remains to be explored.
Publisher: Cold Spring Harbor Laboratory
Date: 13-04-2021
DOI: 10.1101/2021.04.08.21255178
Abstract: Develop an extract, transform, load (ETL) framework for the conversion of health databases to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) that supports transparency of the mapping process, readability, refactoring, and maintainability. We propose an ETL framework that is metadata-driven and generic across source datasets. The ETL framework reads mapping logic for OMOP tables from YAML files, which organize SQL snippets in key-value pairs that define the extract and transform logic to populate OMOP columns. We developed a data manipulation language (DML) for writing the mapping logic from health datasets to OMOP, which defines mapping operations on a column-by-column basis. A core ETL pipeline converts the DML in YAML files and generates an ETL script. We provide access to our ETL framework via a web application, allowing users to upload and edit YAML files and obtain an ETL SQL script that can be used in development environments. The structure of the DML and the mapping operations defined in column-by-column operations maximizes readability, refactoring, and maintainability, while minimizing technical debt, and standardizes the writing of ETL operations for mapping to OMOP. Our web application allows institutions and teams to reuse the ETL pipeline by writing their own rules using our DML. The research community needs tools that reduce the cost and time effort needed to map datasets to OMOP. These tools must support transparency of the mapping process for mapping efforts to be reused by different institutions.
Publisher: Springer Science and Business Media LLC
Date: 22-11-2019
DOI: 10.1038/S41746-019-0190-1
Abstract: Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.
Publisher: JMIR Publications Inc.
Date: 08-12-2020
DOI: 10.2196/19991
Abstract: Smartphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users. This 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features. This was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups—underweight-normal and overweight-obese BMI—using t tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis. In total, 55 participants were recruited (mean age of 23.6, SD 4.6 years 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants’ BMI decreased by 0.34 kg/m2 (P .001), with a loss of 0.86 kg/m2 in the overweight-obese group (P=.01). Participants in the overweight-obese group used the app significantly less compared with in iduals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app’s self-monitoring and feedback (P=.02) and social (P=.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370 P .001). Most participants mentioned a desire for a more personalized intervention. This study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match in idual preferences and needs.
Publisher: Oxford University Press (OUP)
Date: 03-03-2023
Abstract: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication. MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 in iduals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740–0.806) and 0.759 (95% CI: 0.726–0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139–0.140), P & 0.0001. ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention. This review was registered with PROSPERO (CRD42020220811).
Publisher: Elsevier BV
Date: 07-2021
Publisher: American Society of Mechanical Engineers
Date: 13-11-2015
Abstract: The use of classification techniques for machine health monitoring and fault diagnosis has been popular in recent years. System response in form of time series data can be used to identify type of defect, severity of defect etc. However, a central issue with time series classification is that of identifying appropriate features for classification. In this paper, we explore a new feature set based on a delay differential equations (DDEs). DDEs have been used recently for extracting features for classification but have never been used to classify system responses. The Duffing oscillator and Van der Pol–Duffing (VDP-D) oscillator are used as dynamic systems, and the responses are classified into self-similar groups. Responses with the same period should belong to the same group. Misclassification rate is used as an indicator of the efficacy of the feature set. The proposed feature set is compared to a statistical feature set, a power spectral coefficient feature set and a wavelet coefficient feature set. In work described in this paper, a density estimation algorithm called DBSCAN is used as the classification algorithm. The proposed DDE-based feature set is found to be significantly better than the other feature sets for the classifying responses generated by the Duffing system. The wavelet and the power spectral coefficient data sets are not found to be significantly better than the statistical feature set for the Duffing system. None of the feature sets tested are discerning enough on the VDP-D system.
Publisher: JMIR Publications Inc.
Date: 09-02-2020
DOI: 10.2196/15823
Abstract: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used ex les include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. Our results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types.
Publisher: JMIR Publications Inc.
Date: 08-05-2020
Abstract: martphone apps, fitness trackers, and online social networks have shown promise in weight management and physical activity interventions. However, there are knowledge gaps in identifying the most effective and engaging interventions and intervention features preferred by their users. his 6-month pilot study on a social networking mobile app connected to wireless weight and activity tracking devices has 2 main aims: to evaluate changes in BMI, weight, and physical activity levels in users from different BMI categories and to assess user perspectives on the intervention, particularly on social comparison and automated self-monitoring and feedback features. his was a mixed methods study involving a one-arm, pre-post quasi-experimental pilot with postintervention interviews and focus groups. Healthy young adults used a social networking mobile app intervention integrated with wireless tracking devices (a weight scale and a physical activity tracker) for 6 months. Quantitative results were analyzed separately for 2 groups—underweight-normal and overweight-obese BMI—using i t /i tests and Wilcoxon sum rank, Wilcoxon signed rank, and chi-square tests. Weekly BMI change in participants was explored using linear mixed effects analysis. Interviews and focus groups were analyzed inductively using thematic analysis. n total, 55 participants were recruited (mean age of 23.6, SD 4.6 years 28 women) and 45 returned for the final session (n=45, 82% retention rate). There were no differences in BMI from baseline to postintervention (6 months) and between the 2 BMI groups. However, at 4 weeks, participants’ BMI decreased by 0.34 kg/m sup /sup ( i P /i & .001), with a loss of 0.86 kg/m sup /sup in the overweight-obese group ( i P /i =.01). Participants in the overweight-obese group used the app significantly less compared with in iduals in the underweight-normal BMI group, as they mentioned negative feelings and demotivation from social comparison, particularly from upward comparison with fitter people. Participants in the underweight-normal BMI group were avid users of the app’s self-monitoring and feedback ( i P /i =.02) and social ( i P /i =.04) features compared with those in the overweight-obese group, and they significantly increased their daily step count over the 6-month study duration by an average of 2292 steps (95% CI 898-3370 i P /i & .001). Most participants mentioned a desire for a more personalized intervention. his study shows the effects of different interventions on participants from higher and lower BMI groups and different perspectives regarding the intervention, particularly with respect to its social features. Participants in the overweight-obese group did not sustain a short-term decrease in their BMI and mentioned negative emotions from app use, while participants in the underweight-normal BMI group used the app more frequently and significantly increased their daily step count. These differences highlight the importance of intervention personalization. Future research should explore the role of personalized features to help overcome personal barriers and better match in idual preferences and needs.
Publisher: Elsevier BV
Date: 09-2022
DOI: 10.1016/J.HLC.2022.04.049
Abstract: To investigate clinical and health system factors associated with receiving catheter ablation (CA) and earlier ablation for non-valvular atrial fibrillation (AF). We used hospital administrative data linked with death registrations in New South Wales, Australia for patients with a primary diagnosis of AF between 2009 and 2017. Outcome measures included receipt of CA versus not receiving CA during follow-up (using Cox regression) and receipt of early ablation (using logistic regression). Cardioversion during index admission (hazard ratio [HR] 1.96 95% CI 1.75-2.19), year of index admission (HR 1.07 95% CI 1.05-1.10), private patient status (HR 2.65 95% CI 2.35-2.97), and living in more advantaged areas (HR 1.18 95% CI 1.13-1.22) were associated with a higher likelihood of receiving CA. A history of congestive heart failure, hypertension, diabetes, and myocardial infarction were associated with a lower likelihood of receiving CA. Private patient status (odds ratio [OR] 2.04 95% CI 1.59-2.61), cardioversion during index admission (OR 1.25 95% CI 1.0-1.57), and history of diabetes (OR 1.6 95% CI 1.06-2.41) were associated with receiving early ablation. Beyond clinical factors, private patients are more likely to receive CA and earlier ablation than their public counterparts. Whether the earlier access to ablation procedures in private patients is leading to differences in outcomes among patients with atrial fibrillation remains to be explored.
Publisher: JMIR Publications Inc.
Date: 18-03-2021
Abstract: utomatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. his study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from in idual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. hest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. he LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses. e performed a retrospective in China. This multicentre study was approved by the institutional review board of the principal investigator’s hospital. Informed consent from patients was exempted due to the retrospective nature of this study.
Publisher: ACM
Date: 25-04-2020
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 18-07-2017
Publisher: ACM
Date: 11-09-2017
Publisher: ACM
Date: 07-07-2012
Publisher: IGI Global
Date: 04-2011
Abstract: The role of collaboration in the realm of social creativity has been the focus of cutting edge research in design studies. In this paper, the authors investigate the role of collaboration in the process of creative design and propose a computational model of creativity based on the newly proposed meta-design approach. Meta-design is a unique participatory approach to design that deals with opening up of design solution spaces, and is aimed at creating a viable social platform for collaborative design. A meta-design-based collaborative approach to the design process may achieve ET-creativity by expanding the conceptual space of design beyond what would have been possible by in idual, non-collaborative design. The model has been implemented using interactive genetic algorithms, which casts the design problem as an optimization problem and uses a set of collaborative users for subjective fitness evaluation. The design problems investigated include the collaborative design of architectural floorplans and editorial design of brochures.
Publisher: Wiley
Date: 11-2007
Publisher: JMIR Publications Inc.
Date: 09-08-2019
Abstract: onversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used ex les include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. his study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. e followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. he 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. ur results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types.
Publisher: Elsevier BV
Date: 02-2018
Publisher: Oxford University Press (OUP)
Date: 22-04-2019
DOI: 10.1093/JAMIA/OCZ046
Abstract: The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient’s present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. Primary care consultations do not appear to follow a classic linear model of defined information seeking activities rather, they are fragmented, highly interdependent, and can be reactively triggered. The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries.
Publisher: IEEE
Date: 08-2007
Publisher: ACM
Date: 07-07-2007
Publisher: JMIR Publications Inc.
Date: 11-02-2021
DOI: 10.2196/24572
Abstract: COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. Clinical data—including demographics, signs, symptoms, comorbidities, and blood test results—and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four overs ling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although overs ling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848 sensitivity 0.455 specificity 0.906), imaging features (AUC 0.926 sensitivity 0.818 specificity 0.901), and a combination of clinical and imaging features (AUC 0.950 sensitivity 0.764 specificity 0.919). The synthetic minority overs ling method further improved the performance of the model using combined features (AUC 0.960 sensitivity 0.845 specificity 0.929). Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.
Publisher: Cold Spring Harbor Laboratory
Date: 04-08-2020
DOI: 10.1101/2020.08.03.20167007
Abstract: Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from in idual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 09-2007
Publisher: Public Library of Science (PLoS)
Date: 11-04-2022
DOI: 10.1371/JOURNAL.PONE.0266911
Abstract: Common data models standardize the structures and semantics of health datasets, enabling reproducibility and large-scale studies that leverage the data from multiple locations and settings. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is one of the leading common data models. While there is a strong incentive to convert datasets to OMOP, the conversion is time and resource-intensive, leaving the research community in need of tools for mapping data to OMOP. We propose an extract, transform, load (ETL) framework that is metadata-driven and generic across source datasets. The ETL framework uses a new data manipulation language (DML) that organizes SQL snippets in YAML. Our framework includes a compiler that converts YAML files with mapping logic into an ETL script. Access to the ETL framework is available via a web application, allowing users to upload and edit YAML files via web editor and obtain an ETL SQL script for use in development environments. The structure of the DML maximizes readability, refactoring, and maintainability, while minimizing technical debt and standardizing the writing of ETL operations for mapping to OMOP. Our framework also supports transparency of the mapping process and reuse by different institutions.
Publisher: IEEE
Date: 2007
Publisher: JMIR Publications Inc.
Date: 25-09-2020
Abstract: OVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. his study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. linical data—including demographics, signs, symptoms, comorbidities, and blood test results—and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four overs ling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. maging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although overs ling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848 sensitivity 0.455 specificity 0.906), imaging features (AUC 0.926 sensitivity 0.818 specificity 0.901), and a combination of clinical and imaging features (AUC 0.950 sensitivity 0.764 specificity 0.919). The synthetic minority overs ling method further improved the performance of the model using combined features (AUC 0.960 sensitivity 0.845 specificity 0.929). linical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.
No related grants have been discovered for Juan Quiroz.