arXiv Machine Learning By Shuhe Wang, Matthew T. Slaughter, Jennifer C. Nelson, Brian D. Williamson

Using binary silver labels in electronic health records-based computable phenotyping algorithms

Read the original on arXiv Machine Learning →

arXiv:2607. 18431v1 Announce Type: cross Abstract: Gold-standard phenotype labels are often unavailable at scale in electronic health record (EHR) studies because they require manual chart review.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 21

Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records

arXiv:2608. 20315v1 Announce Type: new Abstract: Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events.

By Jun Ni Du, Lukas Adamek, Maxim Kryukov, Flavio Dormont, Ziv Bar-Joseph, Sven Jager, Brandon Rufino
arXiv Machine Learning
Sep 18

Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization

The paper introduces a statistically grounded framework for interpretable, rule-based clinical classification using Bernoulli Naïve Bayes (BNB). It employs supervised chi‑square‑guided binarization to convert continuous medical variables into binary indicators, enabling BNB to handle continuous data while maintaining transparency. On three benchmark datasets—Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction—the method achieved AUCs of 0.800, 0.984, and 0.919, respectively, and demonstrated reliable probability calibration through cross‑validated analysis and post‑hoc beta calibration.

By Antony Garcia, Adrian Noriega, Gabrielle Britton, Xinming Huang
arXiv Machine Learning
Aug 4

xMICD: Explainable Representation of Multiple ICD Codes

arXiv:2608. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.

By Pat Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong, Nutcha Yodrabum, Wasin Pansiritanachot, Marvin N. Wright, Thanapon Noraset