Publications by authors named "M A Israel"
Implementation of a Machine Learning Risk Prediction Model for Postpartum Depression in the Electronic Health Records.
AMIA Jt Summits Transl Sci Proc
October 2024
This study describes the deployment process of an AI-driven clinical decision support (CDS) system to support postpartum depression (PPD) prevention, diagnosis and management. Central to this CDS is an L2-regularized logistic regression model trained on electronic health record (EHR) data at an academic medical center, and subsequently refined through a broader dataset from a consortium to ensure its generalizability and fairness. The deployment architecture leveraged Microsoft Azure to facilitate a scalable, secure, and efficient operational framework.
View Article and Find Full Text PDFObjective: Previous studies have indicated that virtual treatments for eating disorders (EDs) are roughly as effective as are in-person treatments; the present nonrandomized study aimed to expand on the current body of evidence by comparing outcomes from a virtual day treatment program with those of an in-person program in an adult ED sample.
Method: Participants were 109 patients who completed at least 60% of day treatment sessions (n = 55 in-person and n = 54 virtual). Outcome measures included ED and comorbid symptoms, and motivation.
Background: Uremic stomatitis is often unfamiliar to healthcare professionals. This study presents five cases of uremic stomatitis, providing a comprehensive analysis of their demographic distribution, clinicopathological features, and management strategies based on existing literature.
Methods: Data were collected from centers across Brazil, Argentina, Venezuela, and Mexico.
Article Synopsis
- * Conducted from December 2022 to June 2023, the research involved two hospitals and treated a total of 37 patients using VCS, with varying combinations of additional procedures.
- * The findings suggest that VCS can significantly improve patient outcomes and reduce healthcare costs, while also enhancing local neurosurgical capabilities in these regions.
Background: To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems.
Objectives: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems.