TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records
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.
Cite
Text
Chen et al. "TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1252Markdown
[Chen et al. "TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/chen2025ijcai-timelymed/) doi:10.24963/IJCAI.2025/1252BibTeX
@inproceedings{chen2025ijcai-timelymed,
title = {{TimelyMed: AI-Driven Clinical Course Attribution and Temporal Mapping for Psychiatric Medical Records}},
author = {Chen, Chien-Hung and Wu, Chi-Shin and Su, Chu-Hsien and Chen, Hsin-Hsi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {11013-11016},
doi = {10.24963/IJCAI.2025/1252},
url = {https://mlanthology.org/ijcai/2025/chen2025ijcai-timelymed/}
}