April 24, 2024, 4:42 a.m. | Mikkel Odgaard, Kiril Vadimovic Klein, Sanne M{\o}ller Thysen, Espen Jimenez-Solem, Martin Sillesen, Mads Nielsen

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.15201v1 Announce Type: new
Abstract: BERT-based models for Electronic Health Records (EHR) have surged in popularity following the release of BEHRT and Med-BERT. Subsequent models have largely built on these foundations despite the fundamental design choices of these pioneering models remaining underexplored. To address this issue, we introduce CORE-BEHRT, a Carefully Optimized and Rigorously Evaluated BEHRT. Through incremental optimization, we isolate the sources of improvement for key design choices, giving us insights into the effect of data representation and individual …

abstract arxiv bert core cs.lg design ehr electronic electronic health records fundamental health issue records release type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne