Abstract
Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture
Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp 1-1
12 Oct 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Interpreting clinical narratives is critical, yet single-agent LLMs are brittle and create a single point of failure. We present a collaborative MAS that models a clinical team to detect problems using only S+O—enforcing inference rather than A/P keyword lookup.
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Details
- Title
- Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture
- Creators
- Yeawon Lee - Drexel UniversityXiaoyang Wang - Drexel UniversityChristopher Yang - Drexel University
- Publication Details
- Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp 1-1
- Conference
- BCB '25: 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics
- Series
- ACM Conferences
- Publisher
- ACM; NEW YORK
- Number of pages
- 1
- Resource Type
- Abstract
- Language
- English
- Academic Unit
- Information Science; College of Computing and Informatics
- Web of Science ID
- WOS:001658892200090
- Scopus ID
- 2-s2.0-105025568347
- Other Identifier
- 991022145523904721
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- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics