Syed Sibte Raza Abidi

@article{nokey,

title = {Plausible Reasoning over Large Health Datasets: A Novel Approach to Data Analytics Leveraging Semantics},

author = {Hossein Mohammadhassanzadeh and Samina Raza Abidi and Syed Sibte Raza Abidi},

doi = {10.1016/j.knosys.2024.111493},

year  = {2024},

date = {2024-02-11},

urldate = {2024-02-11},

journal = {Knowledge-Based Systems},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Spatial Hotspots and Sociodemographic Profiles Associated with Traumatic Brain Injury in Nova Scotia},

author = {Nelofar Kureshi and Syed Sibte Raza Abidi and David B Clarke and Weiping Zeng and Cindy Feng},

doi = {10.1089/neu.2023.0257},

year  = {2023},

date = {2023-12-04},

urldate = {2023-12-04},

journal = {Journal of Neurotrauma},

abstract = {Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions. While many TBIs are preventable, there is a notable lack of studies exploring the association of geographically defined TBI hotspots with social deprivation. Geographic information systems (GIS) can be used to identify at-risk neighborhoods (hotspots) for targeted interventions. This study aims to determine the spatial distribution of TBI by major causes and to explore the sociodemographic and economic characteristics of TBI hotspots and coldspots in Nova Scotia. Patient data for TBIs from 2003 to 2019 were obtained from the Nova Scotia Trauma Registry. Residential postal codes were geocoded and assigned to Dissemination Areas (DA). Area-based risk factors and deprivation status (residential instability [RI], economic dependency [ED], ethnocultural composition [EC], and situational vulnerability [SV]) from the national census data were linked to DAs. Spatial autocorrelation was assessed using Moran’s I, and hotspot analysis was performed using Getis-Ord Gi* statistic. Differences in risk factors between hot and cold spots were evaluated using the Mann-Whitney U-test for numerical variables and the Chi-square test or Fisher’s Exact test for categorical variables. A total of 5394 TBI patients were eligible for inclusion in the study. The distribution of hotspots for falls exhibited no significant difference between urban and rural areas (p=0.71). Conversely, hotspots related to violence were predominantly urban (p=0.001), while hotspots for motor vehicle collisions (MVCs) were mostly rural (p<0.001). Distinct dimensions of deprivation were associated with falls, MVC, and violent hotspots. Fall hotspots were significantly associated with areas characterized by higher RI (p <0.001) and greater ethnocultural diversity (p <0.001). Conversely, the same domains exhibited an inverse relationship with MVC hotspots; areas with low RI and ethnic homogeneity displayed a higher proportion of MVC hotspots. ED and SV exhibited a strong gradient with MVC hotspots; the most deprived quintiles displayed the highest proportion of MVC hotspots compared to coldspots (ED; p = 0.002, SV; p < 0.001). Areas with the highest levels of ethnocultural diversity were found to have a significantly higher proportion of violence-related hotspots compared to coldspots (p = 0.005). This study offers two significant contributions to spatial epidemiology. Firstly, it demonstrates the distribution of TBI hotspots by major injury causes using the smallest available geographical unit. Secondly, we disentangle the various pathways through which deprivation impacts the risk of main mechanisms of TBI. These findings provide valuable insights for public health officials to design targeted injury prevention strategies in high-risk areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Needs and expectations for artificial intelligence in emergency medicine according to Canadian physicians},

author = {Kyle W. Eastwood and Ronald May and Pantelis Andreou and Samina Abidi and Syed Sibte Raza Abidi and Osama M. Loubani},

doi = {https://doi.org/10.1186/s12913-023-09740-w},

issn = {1472-6963},

year  = {2023},

date = {2023-07-25},

urldate = {2023-07-25},

journal = {BMC Health Services Research},

volume = {23},

number = {798},

abstract = {Background

Artificial Intelligence (AI) is recognized by emergency physicians (EPs) as an important technology that will affect clinical practice. Several AI-tools have already been developed to aid care delivery in emergency medicine (EM). However, many EM tools appear to have been developed without a cross-disciplinary needs assessment, making it difficult to understand their broader importance to general-practice. Clinician surveys about AI tools have been conducted within other medical specialties to help guide future design. This study aims to understand the needs of Canadian EPs for the apt use of AI-based tools.



Methods

A national cross-sectional, two-stage, mixed-method electronic survey of Canadian EPs was conducted from January-May 2022. The survey includes demographic and physician practice-pattern data, clinicians’ current use and perceptions of AI, and individual rankings of which EM work-activities most benefit from AI.



Results

The primary outcome is a ranked list of high-priority AI-tools for EM that physicians want translated into general use within the next 10 years. When ranking specific AI examples, ‘automated charting/report generation’, ‘clinical prediction rules’ and ‘monitoring vitals with early-warning detection’ were the top items. When ranking by physician work-activities, ‘AI-tools for documentation’, ‘AI-tools for computer use’ and ‘AI-tools for triaging patients’ were the top items. For secondary outcomes, EPs indicated AI was ‘likely’ (43.1%) or ‘extremely likely’ (43.7%) to be able to complete the task of ‘documentation’ and indicated either ‘a-great-deal’ (32.8%) or ‘quite-a-bit’ (39.7%) of potential for AI in EM. Further, EPs were either ‘strongly’ (48.5%) or ‘somewhat’ (39.8%) interested in AI for EM.



Conclusions

Physician input on the design of AI is essential to ensure the uptake of this technology. Translation of AI-tools to facilitate documentation is considered a high-priority, and respondents had high confidence that AI could facilitate this task. This study will guide future directions regarding the use of AI for EM and help direct efforts to address prevailing technology-translation barriers such as access to high-quality application-specific data and developing reporting guidelines for specific AI-applications. With a prioritized list of high-need AI applications, decision-makers can develop focused strategies to address these larger obstacles.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Community-of-Practice-based Evaluation Methodology for Knowledge Intensive Computational Methods and its Application to Multimorbidity Decision Support},

author = {William Van Woensel and Samson W. Tu and Wojtek Michalowski and Syed Sibte Raza Abidi and Samina Abidi and Jose-Ramon Alonso and Alessio Bottrighi and Marc Carrier and Ruth Edry and Irit Hochberg and Malvika Rao and Stephen Kingwell and Alexandra Kogan and Mar Marcos and Begoña Martínez Salvador and Martin Michalowski and Luca Piovesan and David Riaño and Paolo Terenziani and Szymon Wilk and Mor Peleg},

url = {https://www.sciencedirect.com/science/article/pii/S1532046423001168},

doi = {https://doi.org/10.1016/j.jbi.2023.104395},

issn = {1532-0464},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Journal of Biomedical Informatics},

volume = {142},

number = {104395},

abstract = {Objective



The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans.



Materials and Methods



Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods—research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods.



Results



Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support.Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods.



Discussion



The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS.



Conclusion



We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS).



Keywords: Evaluation Study; Benchmarking; Multimorbidity; Decision Support Systems, Clinical; Computer-interpretable Clinical Guidelines},

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pubstate = {published},

tppubtype = {article}

}

@article{info:doi/10.2196/44370,

title = {Improving Mental Health Literacy and Reducing Psychological Problems Among Teachers in Zambia: Protocol for Implementation and Evaluation of a Wellness4Teachers Email Messaging Program},

author = {Belinda Agyapong and Charles Chishimba and Yifeng Wei and Raquel Luz Dias and Ejemai Eboreime and Eleanor Msidi and Syed Sibte Raza Abidi and Maryn Mutaka-Loongo and James Mwansa and Rita Orji and John Mathias Zulu and Vincent Israel Opoku Agyapong},

url = {http://www.ncbi.nlm.nih.gov/pubmed/36877571},

doi = {10.2196/44370},

issn = {1929-0748},

year  = {2023},

date = {2023-03-06},

urldate = {2023-03-06},

journal = {JMIR Res Protoc},

volume = {12},

pages = {e44370},

abstract = {Background: Primary, basic, secondary, and high school teachers are constantly faced with increased work stressors that can result in psychological health challenges such as burnout, anxiety, and depression, and in some cases, physical health problems. It is presently unknown what the mental health literacy levels are or the prevalence and correlates of psychological issues among teachers in Zambia. It is also unknown if an email mental messaging program (Wellness4Teachers) would effectively reduce burnout and associated psychological problems and improve mental health literacy among teachers. Objective: The primary objectives of this study are to determine if daily supportive email messages plus weekly mental health literacy information delivered via email can help improve mental health literacy and reduce the prevalence of moderate to high stress symptoms, burnout, moderate to high anxiety symptoms, moderate to high depression symptoms, and low resilience among school teachers in Zambia. The secondary objectives of this study are to evaluate the baseline prevalence and correlates of moderate to high stress, burnout, moderate to high anxiety, moderate to high depression, and low resilience among school teachers in Zambia. Methods: This is a quantitative longitudinal and cross-sessional study. Data will be collected at the baseline (the onset of the program), 6 weeks, 3 months, 6 months (the program midpoint), and 12 months (the end point) using web-based surveys. Individual teachers will subscribe by accepting an invitation to do so from the Lusaka Apex Medical University organizational account on the ResilienceNHope web-based application. Data will be analyzed using SPSS version 25 with descriptive and inferential statistics. Outcome measures will be evaluated using standardized rating scales. Results: The Wellness4Teachers email program is expected to improve the participating teachers' mental health literacy and well-being. It is anticipated that the prevalence of stress, burnout, anxiety, depression, and low resilience among teachers in Zambia will be similar to those reported in other jurisdictions. In addition, it is expected that demographic, socioeconomic, and organizational factors, class size, and grade teaching will be associated with burnout and other psychological disorders among teachers, as indicated in the literature. Results are expected 2 years after the program's launch. Conclusions: The Wellness4Teachers email program will provide essential insight into the prevalence and correlates of psychological problems among teachers in Zambia and the program's impact on subscribers' mental health literacy and well-being. The outcome of this study will help inform policy and decision-making regarding psychological interventions for teachers in Zambia. International Registered Report Identifier (IRRID): PRR1-10.2196/44370},

keywords = {burnout; stress; Zambia; Africa; teacher; educator; school; anxiety; wellness; depression; e-mental health; intervention; health literacy; mental health; depressive; psychological issue; psychological problem; text message; messaging; decision-making},

pubstate = {published},

tppubtype = {article}

}

@article{https://doi.org/10.1002/jor.25363,

title = {Gait biomechanics phenotypes among total knee arthroplasty candidates by machine learning cluster analysis},

author = {Kathryn L. Young-Shand and Patrice C. Roy and Michael J. Dunbar and Syed S. R. Abidi and Janie L. Astephen Wilson},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jor.25363},

doi = {https://doi.org/10.1002/jor.25363},

year  = {2022},

date = {2022-05-10},

urldate = {2022-05-10},

journal = {Journal of Orthopaedic Research},

abstract = {Abstract Knee osteoarthritis patient phenotyping is relevant to developing targeted treatments and assessing the treatment efficacy of total knee arthroplasty (TKA). This study aimed to identify clusters among TKA candidates based on demographic and knee mechanic features during gait, and compare gait changes between clusters postoperatively. TKA patients underwent 3D gait analysis 1-week pre (n = 134) and 1-year post-TKA (n = 105). Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms, extracting major patterns of variability. Age, sex, body mass index, gait speed, and frontal and sagittal pre-TKA angle and moment PC scores previously identified as relevant to TKA outcomes were standardized (mean = 0},

keywords = {gait analysis, knee biomechanics, Machine Learning, phenotypes, Total knee arthroplasty},

pubstate = {published},

tppubtype = {article}

}

@article{davis2022,

title = {Semantic Knowledge Modeling and Evaluation of Argument Theory to Develop Dialogue based Patient Education Systems for Chronic Disease Self-Management},

author = {Benjamin Rose-Davis and William Van Woensel and Samina Raza Abidi and Elizabeth Stringer and Syed Sibte Raza Abidi},

url = {https://www.sciencedirect.com/science/article/abs/pii/S1386505622000077},

year  = {2022},

date = {2022-01-19},

urldate = {2022-01-18},

journal = {International Journal of Medical Informatics},

keywords = {Argument Theory, Knowledge Graphs, Patient Education, Semantic Web},

pubstate = {published},

tppubtype = {article}

}

@article{Naqvi2021b,

title = {Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study},

author = {Syed Asil Ali Naqvi and Karthik Tennankore and Amanda Vinson and Patrice C Roy and Syed Sibte Raza Abidi},

url = {https://www.jmir.org/2021/8/e26843},

doi = {10.2196/26843},

issn = {1438-8871},

year  = {2021},

date = {2021-08-27},

journal = {Journal of Medical Internet Research},

volume = {23},

number = {8},

pages = {e26843},

publisher = {Journal of Medical Internet Research},

abstract = {Background: Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation. 

Objective: The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period. 

Methods: We applied machine learning–based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning–based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach. 

Results: Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts. 

Conclusions: In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.},

keywords = {dimensionality reduction, feature sensitivity analysis, survival prediction},

pubstate = {published},

tppubtype = {article}

}

@article{VanWoensel2021b,

title = {Staged Reflexive Artificial Intelligence Driven Testing Algorithms for Early Diagnosis of Pituitary Disorders},

author = {William Van Woensel and Manal Elnenaei and Syed Sibte Raza Abidi and David B Clarke and Syed Ali Imran},

url = {https://www.sciencedirect.com/science/article/pii/S0009912021002265},

year  = {2021},

date = {2021-08-20},

urldate = {2021-08-20},

journal = {Clinical Biochemistry},

keywords = {Clinical Decision Support Systems, Health Informatics, Reflex Protocols},

pubstate = {published},

tppubtype = {article}

}

@article{VANWOENSEL2021102127,

title = {Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning},

author = {William Van Woensel and Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {https://www.sciencedirect.com/science/article/pii/S0933365721001202},

doi = {https://doi.org/10.1016/j.artmed.2021.102127},

issn = {0933-3657},

year  = {2021},

date = {2021-08-01},

journal = {Artificial Intelligence in Medicine},

volume = {118},

pages = {102127},

abstract = {In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks—these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.},

keywords = {Clinical guidelines, Comorbidity, Decision Support Systems},

pubstate = {published},

tppubtype = {article}

}

@article{Naseem2020,

title = {Factors enabling and hindering an eLearning programme for nurses and midwives in Afghanistan},

author = {Azra Naseem and Kiran {Qasim Ali} and Audrey Juma and Afroz Sajwani and Basnama Ayaz Khan and Saleem Sayani and Syed Sibte Raza Abidi},

url = {https://sotl-south-journal.net/?journal=sotls&page=article&op=view&path%5B%5D=106},

doi = {10.36615/sotls.v4i2.106},

issn = {2523-1154},

year  = {2020},

date = {2020-09-01},

journal = {Scholarship of Teaching and Learning in the South},

volume = {4},

number = {2},

pages = {80},

abstract = {textlessptextgreaterAfghanistan faces an acute shortage of trained healthcare providers. To build capacity of nurses and midwives, in 2014 a private hospital in Afghanistan initiated an eLearning programme to enhance their knowledge and skills. The study was conducted to identify facilitating and hindering factors for the successful implementation of eLearning. Data collection took place between June and September 2016, when seven Maternal and Child Health (MNCH) related eLearning sessions were conducted. The participants were nurses and midwives working in MNCH wards at the research sites in Bamyan, Faizabad and Kandahar, along with the programme planners and facilitators. Data was collected through pre/post and delayed post-tests, observations and questionnaires, semi-structured interviews and documents analysis. The results highlight four major factors as important for the successful implementation of eLearning, namely: curriculum, context, technology and individual. The needs assessment ensured relevance of the sessions to the needs of the participants. However, pedagogy was lecture-based with limited focus on skills development. Poor connectivity and language of instruction posed challenges. eLearning has shown the potential for developing knowledge and skills of nurses and midwives. Clear communication between teams involved in planning and implementation of the programme, technology infrastructure, design of online pedagogy and facilitator readiness are critical for the success of eLearning in low and middle income countries.
 Keywords: Health care providers/system, eLearning Programme, Nurses, Midwives, Maternal and child careHow to cite this article:Naseem, A., Ali, K.Q., Juma, A., Sajwani, A., Khan, B.A., Sayani, A. & Abidi, S.S.R. 2020. Factors enabling and hindering an eLearning programme for nurses and midwives in Afghanistan. Scholarship of Teaching and Learning in the South. 4(2): 80-99. https://doi.org/10.36615/sotls.v4i2.106.This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/textless/ptextgreater},

keywords = {eLearning, Learning technology},

pubstate = {published},

tppubtype = {article}

}

@article{VANWOENSEL2020101931,

title = {Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods},

author = {William Van Woensel and Patrice C Roy and Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {http://www.sciencedirect.com/science/article/pii/S0933365720301275

https://authors.elsevier.com/a/1bTwR3KEGaD3xR},

doi = {https://doi.org/10.1016/j.artmed.2020.101931},

issn = {0933-3657},

year  = {2020},

date = {2020-01-01},

journal = {Artificial Intelligence in Medicine},

volume = {108},

pages = {101931},

abstract = {In a digitally enabled healthcare setting, we posit that an individual’s current location is pivotal for supporting many virtual care services—such as tailoring educational content towards an individual’s current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual’s indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual’s discrete semantic location; i.e., it is the concrete type of an individual’s location (e.g., exam vs. waiting room; bathroom vs. kitchen) that will drive the tailoring of educational content or recognition of activities. We utilized Machine Learning methods to accurately identify an individual’s discrete location, together with knowledge-based models and tools to supply the associated semantics of identified locations. We considered clustering solutions to improve localization accuracy at the expense of granularity; and investigate sensor fusion-based heuristics to rule out false location estimates. We present an AI-driven indoor localization approach that integrates both data-driven and knowledge-based processes and artifacts. We illustrate the application of our approach in two compelling healthcare use cases, and empirically validated our localization approach at the emergency unit of a large Canadian pediatric hospital.},

keywords = {Activities of daily living, Ambient assisted living, Ambient Intelligence, Ambient sensors, Chronic disease self-management, Data fusion, eHealth Platform, Indoor Localization, Machine Learning, Self-Management, Semantic Web, Virtual care},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/0840470419846134,

title = {Intelligent health data analytics: A convergence of artificial intelligence and big data},

author = {Syed Sibte Raza Abidi and Samina Raza Abidi},

url = {https://doi.org/10.1177/0840470419846134},

doi = {10.1177/0840470419846134},

year  = {2019},

date = {2019-05-22},

journal = {Healthcare Management Forum},

volume = {32},

number = {4},

pages = {178--182},

abstract = {Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}