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Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches

The purpose of this analysis was to build on this work by investigating the feasibility of using ML methods to classify self-reported RA flares based on a small data set of daily symptom data collected through a smartphone app.

Julie Gandrup, David A Selby, William G Dixon

JMIR Form Res 2024;8:e50679

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Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

To illustrate the impact of these differences, we considered the impact of implementing each of these phenotypes at a 90% sensitivity to classify patients during the January Omicron wave. Within our health system, 1378 people were hospitalized and tested positive for SARS-Co V-2. Based on our analyses, using the LASSO-based phenotype that incorporates structured data, clinical notes, or their combination would result in approximately 244, 165, and 142 false positives, respectively.

Feier Chang, Jay Krishnan, Jillian H Hurst, Michael E Yarrington, Deverick J Anderson, Emily C O'Brien, Benjamin A Goldstein