ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Integrating Remote Patient Monitoring Data into Machine Learning Models for Predicting Emergency Department Utilization.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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Abstract
The integration of Remote Patient Monitoring (RPM) data into risk stratification models has emerged as a promising approach for improving healthcare delivery and patient outcomes. In this work, we explore the integration of RPM features - including at home monitoring of body weight, blood pressure, and blood oxygen - into a machine learning model that uses EHR data to predict the likelihood of emergency department (ED) visits or unplanned inpatient admissions within the next 30 days. Through exploratory data analysis, feature engineering, model training, and evaluation of a dataset with 913 patients, we found that RPM data has signal to predict unplanned utilization, and combining RPM data with EHR data improves the predictive power of the model, compared with either data source alone. We discuss the transformative potential of RPM data to augment predictive analytics capabilities in care management settings.
Indexed as
Identifiers
40417519PMC12099409What OpenQuestion holds
Registered trials
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