Sep 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 33 references
Machine Learning in Healthcare
TL;DR
Findings suggest that drift-triggered temporal and institutional adaptation can improve the robustness and stability of clinical language models under the distribution shifts represented in the evaluated datasets, but further prospective and independent external validation is required before the framework can be considered for routine clinical deployment.
MMTClinic is presented, a benchmark designed to evaluate large language models (LLMs) on complex reasoning and question-answering tasks involving clinical time-series and reveals notable differences in model performance across tasks, languages, and modalities, highlighting current limitations in clinical reasoning capa...
Sourav Malakar, Harshit Nigam, Akash Ghosh et al.· 0 citations
CAST (Concept-guided Artifact Suppression Tuning), an SAE-based framework for auditable clinical text classification, improves over its corresponding fine-tuned encoder baselines and remains competitive with strong LLM baselines, while producing a feature-level audit trail of the clinical concepts that support each pre...
Results show that medical knowledge updating depends not only on the update algorithm, but also on how knowledge is structured as supervision, and suggest that EMQ gives the most stable external transfer and retention among same-budget SFT variants.
Yangmin Huang, Shu Quan, He Geng et al.· 0 citations
By tracing the evolutionary trajectories of these methodologies, this scoping review provides a mechanism-centered framework to inform responsible model development and deployment in medical settings, tailored to task complexity, data characteristics, and resource constraints.
Fang Li, Jian-Fu Li, Weiguo Cao et al.· npj Health Systems· 0 citations
ViSTA is introduced, a compact adapter that incorporates irregular numerical measurements into a pretrained vision-language model's chart representations and extends pretrained language models to numerical prediction and temporal questions.
Jun-Yi Gao, Yu Shi, Ping-Zhao Hu et al.· 0 citations
This tutorial presents OneEHR, an open-source toolkit that defines a unified experiment contract for modern EHR modeling and enables head-to-head comparison among conventional, neural, LLM-based, and agentic methods through a single configuration-driven interface.
Yinghao Zhu, Zi-Xiang Wang, Lei Gu et al.· Proceedings of the 32nd ACM...· 0 citations
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