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Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations

Benchmarking Clinical Speech Recognition and Information Extraction: New Data, Methods, and Evaluations

The first author of this paper (Adj/Prof in machine learning for communication and health computing) considered an imaginary medical ward in Australia. With an aim for balance in patient types, she created simulated profiles for 101 patients.

Hanna Suominen, Liyuan Zhou, Leif Hanlen, Gabriela Ferraro

JMIR Med Inform 2015;3(2):e19


Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy

Based on ambulatory data collected from young controls, we develop linear (regression) and nonlinear (machine-learning–based) models for EE estimation.

Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han

JMIR Rehabil Assist Technol 2016;3(2):e7


The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System

The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System

In this protocol, “Automation” is defined as applications of robotics, artificial intelligence, machine learning, machine vision, and similar emerging and mature digital technologies that will allow human work to be substituted by computer capital.

Matthew Willis, Paul Duckworth, Angela Coulter, Eric T Meyer, Michael Osborne

JMIR Res Protoc 2019;8(4):e11232


Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature

Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature

With the advent of new tools and algorithms for machine learning, a new class of smart digital health interventions can be developed, which could revolutionize effective health care delivery [5].The term machine learning is widely used across disciplines but

Andreas K Triantafyllidis, Athanasios Tsanas

J Med Internet Res 2019;21(4):e12286


Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision

Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision

Processed by advanced computer vision and machine-learning algorithms, depth videos enable the quantification of human movement without the need for marker-based motion capture or gait analysis systems, which are both expensive and cumbersome [1].

Cecily Morrison, Marcus D'Souza, Kit Huckvale, Jonas F Dorn, Jessica Burggraaff, Christian Philipp Kamm, Saskia Marie Steinheimer, Peter Kontschieder, Antonio Criminisi, Bernard Uitdehaag, Frank Dahlke, Ludwig Kappos, Abigail Sellen

JMIR Hum Factors 2015;2(1):e11


Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

Machine learning–based online tools are emerging as an alternative method of predicting provider performance that can factor patient-specific characteristics into provider rankings [17,18].

Dev Goyal, John Guttag, Zeeshan Syed, Rudra Mehta, Zahoor Elahi, Mohammed Saeed

J Med Internet Res 2020;22(12):e22765