Proceedings

TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records

Chien-Hung Chen, Chi-Shin Wu, Chu-Hsien Su, Hsin-Hsi Chen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Demo Track. Pages 11013-11016.

https://doi.org/10.24963/ijcai.2025/1252

Keywords

  • Natural Language Processing: NLP: Information extraction
  • Natural Language Processing: NLP: Language generation
  • Natural Language Processing: NLP: Text classification
  • Natural Language Processing: NLP: Applications

Abstract

Timely understanding of a patient’s clinical course is crucial for effective treatment. Extracting course-related information, such as temporal and medical events, from unstructured medical records is both challenging and time-consuming, especially when relying on manual identification by physicians. We introduce TimelyMed, a system powered by a locally deployed large language model (LLM) that ensures data security while efficiently organizing key psychiatric events and their corresponding temporal information. Additionally, our system is attributed, allowing clinicians to not only categorize events but also trace them back to their original textual descriptions, ensuring transparency and interpretability in clinical decision-making. By organizing temporal and medical event information into timelines, our system enables physicians to quickly grasp a patient’s medical history while effectively reducing their cognitive burden.

Chien-Hung Chen, Chi-Shin Wu, Chu-Hsien Su, and Hsin-Hsi Chen. 2025. TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 11013–11016. https://doi.org/10.24963/ijcai.2025/1252
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