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Piloting a Clinical Decision Support Tool to Identify Patients With Social Needs and Provide Navigation Services and Referral to Community-Based Organizations: Protocol for a Randomized Controlled Trial

Elham Hatef, Thomas Richards, Kristin Topel, Sofia Hail, Christopher Kitchen, Katherine Shaw, Talan Zhang, Elyse C Lasser, Jonathan P Weiner

JMIR Res Protoc 2024;13:e57316

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Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis

Except for those noted in footnote c of Table 1, all characteristics showed statistically significant differences between racial and income groups (all P values Demographic characteristics by race and by income in Maryland (n=1,857,658) and Florida (n=8,733,002). a MD: Maryland. b FL: Florida. c P values were computed between racial groups and between income groups, respectively. All P values are d CCI: Charlson Comorbidity Index.

H Echo Wang, Jonathan P Weiner, Suchi Saria, Hadi Kharrazi

J Med Internet Res 2024;26:e47125

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Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study

Elham Hatef, Hsien-Yen Chang, Thomas M Richards, Christopher Kitchen, Janya Budaraju, Iman Foroughmand, Elyse C Lasser, Jonathan P Weiner

JMIR Form Res 2024;8:e54732

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Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology

F&P: feature selection and predictions; PHU: persistent high utilizer; non-PHU: nonpersistent high utilizer. The models selected as the layers in the ensemble method were chosen using common techniques, namely assessment of common classification algorithms and random search cross-validation for parameter tuning. Typically, machine learning models are assessed for performance and generalizability.

Stephanie N Howson, Michael J McShea, Raghav Ramachandran, Howard S Burkom, Hsien-Yen Chang, Jonathan P Weiner, Hadi Kharrazi

JMIR Med Inform 2022;10(3):e33212

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Assessing the Value of Unsupervised Clustering in Predicting Persistent High Health Care Utilizers: Retrospective Analysis of Insurance Claims Data

Raghav Ramachandran, Michael J McShea, Stephanie N Howson, Howard S Burkom, Hsien-Yen Chang, Jonathan P Weiner, Hadi Kharrazi

JMIR Med Inform 2021;9(11):e31442

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Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

This model significantly (P We evaluated this best-performing CRF model on the test set and report per-construct results in addition to overall averages (see Table 5 and Figure 1). The CRF obtained macroaverage F1 scores of 0.394 for phrase-exact, 0.666 for phrase-partial, 0.759 for note, and 0.834 for patient. Microaverage F1 scores were 0.410 for phrase-exact, 0.661 for phrase-partial, 0.787 for note, and 0.851 for patient.

Tao Chen, Mark Dredze, Jonathan P Weiner, Leilani Hernandez, Joe Kimura, Hadi Kharrazi

JMIR Med Inform 2019;7(1):e13039

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