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The Lancet. Digital Health
|
September 1, 2020
The myth of generalisability in clinical research and machine learning in health care
Joseph Futoma, Morgan Simons, Trishan Panch, et al.
Journal of Computing Science and Engineering : JCSE
|
June 15, 2013
Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System
Leo A Celi, Roger G Mark, Joon Lee, et al.
Journal of Cardiopulmonary Rehabilitation and Prevention
|
May 29, 2014
Utility and efficacy of a smartphone application to enhance the learning and behavior goals of traditional cardiac rehabilitation: a feasibility study
Daniel E Forman, Karen LaFond, Trishan Panch, et al.
NPJ Digital Medicine
|
June 25, 2020
"Yes, but will it work for <i>my</i> patients?" Driving clinically relevant research with benchmark datasets
Trishan Panch, Tom J Pollard, Heather Mattie, et al.
JMIR Mental Health
|
August 17, 2018
Using Smartphone Apps to Promote Psychiatric Rehabilitation in a Peer-Led Community Support Program: Pilot Study
Nora E Mueller, Trishan Panch, Cathaleene Macias, et al.
PLOS Digital Health
|
February 22, 2023
A distributed approach to the regulation of clinical AI
Trishan Panch, Erin Duralde, Heather Mattie, et al.
The Psychiatric Quarterly
|
February 1, 2015
Using Smartphone Apps to Promote Psychiatric and Physical Well-Being
Cathaleene Macias, Trishan Panch, Yale M Hicks, et al.
BMJ Open
|
May 13, 2020
Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol
Rodrigo M Carrillo-Larco, Lorainne Tudor Car, Jonathan Pearson-Stuttard, et al.
The Lancet. Digital Health
|
December 16, 2020
Turning the crank for machine learning: ease, at what expense?
Tom J Pollard, Irene Chen, Jenna Wiens, et al.
Population Health Management
|
November 26, 2019
A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods
Heather Mattie, Patrick Reidy, Patrik Bachtiger, et al.
Page
of 3
Search research articles
Search
Showing results (11-20 of 23) with videos related to
Sort By:
Page
of 3
The Lancet. Digital Health
|
September 1, 2020
The myth of generalisability in clinical research and machine learning in health care
Joseph Futoma, Morgan Simons, Trishan Panch, et al.
Journal of Computing Science and Engineering : JCSE
|
June 15, 2013
Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System
Leo A Celi, Roger G Mark, Joon Lee, et al.
Journal of Cardiopulmonary Rehabilitation and Prevention
|
May 29, 2014
Utility and efficacy of a smartphone application to enhance the learning and behavior goals of traditional cardiac rehabilitation: a feasibility study
Daniel E Forman, Karen LaFond, Trishan Panch, et al.
NPJ Digital Medicine
|
June 25, 2020
"Yes, but will it work for <i>my</i> patients?" Driving clinically relevant research with benchmark datasets
Trishan Panch, Tom J Pollard, Heather Mattie, et al.
JMIR Mental Health
|
August 17, 2018
Using Smartphone Apps to Promote Psychiatric Rehabilitation in a Peer-Led Community Support Program: Pilot Study
Nora E Mueller, Trishan Panch, Cathaleene Macias, et al.
PLOS Digital Health
|
February 22, 2023
A distributed approach to the regulation of clinical AI
Trishan Panch, Erin Duralde, Heather Mattie, et al.
The Psychiatric Quarterly
|
February 1, 2015
Using Smartphone Apps to Promote Psychiatric and Physical Well-Being
Cathaleene Macias, Trishan Panch, Yale M Hicks, et al.
BMJ Open
|
May 13, 2020
Machine learning health-related applications in low-income and middle-income countries: a scoping review protocol
Rodrigo M Carrillo-Larco, Lorainne Tudor Car, Jonathan Pearson-Stuttard, et al.
The Lancet. Digital Health
|
December 16, 2020
Turning the crank for machine learning: ease, at what expense?
Tom J Pollard, Irene Chen, Jenna Wiens, et al.
Population Health Management
|
November 26, 2019
A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods
Heather Mattie, Patrick Reidy, Patrik Bachtiger, et al.
Page
of 3