This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (671) Neurology and Neurosciences (286) Sensors at Home and Domotics (81) Machine Learning (2076)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60437, first published .
Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility Study

Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility Study

Integrating Ambient In-Home Sensor Data and Electronic Health Record Data for the Prediction of Outcomes in Amyotrophic Lateral Sclerosis: Protocol for an Exploratory Feasibility Study

Authors of this article:

William E Janes1 Author Orcid Image ;   Noah Marchal2 Author Orcid Image ;   Xing Song2, 3 Author Orcid Image ;   Mihail Popescu2, 3 Author Orcid Image ;   Abu Saleh Mohammad Mosa2, 3, 4 Author Orcid Image ;   Juliana H Earwood1 Author Orcid Image ;   Vovanti Jones5 Author Orcid Image ;   Marjorie Skubic2, 4 Author Orcid Image
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Journals

  1. Marchal N, Janes W, Marushak S, Popescu M, Song X. Enhancing ALS progression tracking with semi-supervised ALSFRS-R scores estimated from ambient home health monitoring. Frontiers in Digital Health 2025;7 View