A5030697098- Artificial Intelligence in Healthcare and Education
- Telemedicine and Telehealth Implementation
- Mobile Health and mHealth Applications
- Machine Learning in Healthcare
- Digital Mental Health Interventions
- Artificial Intelligence in Healthcare
- Healthcare cost, quality, practices
- Health Systems, Economic Evaluations, Quality of Life
- Chronic Disease Management Strategies
- Clinical Reasoning and Diagnostic Skills
- Ethics in Clinical Research
- Healthcare Policy and Management
- Health disparities and outcomes
- Action Observation and Synchronization
- Eating Disorders and Behaviors
- Electronic Health Records Systems
- AI in cancer detection
- Mental Health and Patient Involvement
- Adolescent and Pediatric Healthcare
- Cardiac Health and Mental Health
- Medical Imaging and Analysis
- Context-Aware Activity Recognition Systems
- Cardiovascular Health and Risk Factors
- Quality and Safety in Healthcare
- Biomedical and Engineering Education
Harvard University
2018-2023
Boston University
2018
Manning Diversified Forest Products (Canada)
2014
Falmouth Hospital
2014
Harvard University Press
2012
AI is a broad discipline that aims to understand and design systems display properties of intelligence (Box 1) -emblematic which the ability learn: derive knowledge from data.This definition arguably has some cross over with existing statistical techniques [6].The recent explosion in
Introduction Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address issues challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version checklist (STARD-AI), which focuses on AI accuracy studies. This paper describes methods that will be used develop STARD-AI....
Artificial Intelligence has been applied in academic research and inference tasks across the broader economy with demonstrable success,1 but less so for core functions of public health, namely protecting promoting health populations.2
Most eligible patients do not participate in traditional clinic-based cardiac rehabilitation (CR) despite well-established benefits. Novel approaches to overcome logistic obstacles and increase efficiencies of learning, behavior modification, exercise surveillance may CR participation. In an observational study, the feasibility utility a mobile smartphone application for CR, Heart Coach (HC), were assessed as part standard care. Ultimately, innovative models incorporating HC facilitate...
Regulation is necessary to ensure the safety, efficacy and equitable impact of clinical artificial intelligence (AI). The number applications AI increasing, which, amplified by need for adaptations account heterogeneity local health systems inevitable data drift, creates a fundamental challenge regulators. Our opinion that, at scale, incumbent model centralized regulation will not efficacy, equity implemented systems. We propose hybrid regulation, where would only be required inference...
Management of severe and persistent mental illness is a complex, resource-intensive challenge for individuals, their families, treaters, the health care system at large. Community-based rehabilitation, in which peer specialists provide support individuals managing own condition, has demonstrated effectiveness but only been implemented specialty centers. It remains unclear how peer-based community rehabilitation model could be expanded, given that it requires significant resources to both...
Digital care management programs can reduce health costs and improve quality of care. However, it is unclear how to target patients who are most likely benefit from these ex ante, a shortcoming current "risk score"-based approaches across many interventions. This study explores framework define impactability by using machine learning (ML) models identify those digital intervention for management. Anonymized insurance claims data were used commercially insured population several US states...
We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models using patients from one's own institution whose features are similar an index patient as regards outcome interest, in order predict utility diagnostic tests and interventions, well inform prognosis. Laboratory Computational Physiology at Massachusetts Institute Technology developed maintains MIMIC-II, public deidentified high- resolution database admitted Beth Israel Deaconess...
Multimorbidity is a defining challenge for health systems and requires coordination of care delivery management. Care management clinical service designed to remotely engage patients between visits after discharge in order support self-management chronic emergent conditions, encourage increased use scheduled address the unscheduled care. can be provided using digital technology - A robust methodology assess management, or any traditional primary intervention aimed at longitudinal...
<sec> <title>BACKGROUND</title> Management of severe and persistent mental illness is a complex, resource-intensive challenge for individuals, their families, treaters, the healthcare system at large. Community based rehabilitation, in which peer specialists provide support individuals managing own condition, has demonstrated effectiveness, but only been implemented specialty centers. It remains unclear how community rehabilitation model could be expanded, given that it requires significant...