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Health Care Professionals and Data Scientists’ Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study

Health Care Professionals and Data Scientists’ Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study

Heart failure (HF) is a complex clinical syndrome characterized by frequent and rapid decompensation; even long-term, stable patients with HF might rapidly deteriorate in days or hours [1]. HF decompensation is a major cause of hospitalization in patients with HF, negatively influencing prognosis [1], representing the highest share of health care costs for this disease [2].

Joana Seringa, Anna Hirata, Ana Rita Pedro, Rui Santana, Teresa Magalhães

J Med Internet Res 2025;27:e54990

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Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

While this study is exploratory, we expect parameters including respiratory rate and heart rate to be predictive of decompensation, considering that they may vary in the degree to which they are predictive for any individual. We hypothesize that a combination of many biosensor-derived physiological features will best capture the heterogenous characteristics of decompensation across the population.

Karen Larimer, Stephan Wegerich, Joel Splan, David Chestek, Heather Prendergast, Terry Vanden Hoek

JMIR Res Protoc 2021;10(5):e27271

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