Bibliographic Details
| Title: |
Using Large Language Models to Retrieve Critical Data from Clinical Processes and Business Rules. |
| Authors: |
Yu, Yunguo, Gomez-Cabello, Cesar A., Makarova, Svetlana, Parte, Yogesh, Borna, Sahar, Haider, Syed Ali, Genovese, Ariana, Prabha, Srinivasagam, Forte, Antonio J. |
| Source: |
Bioengineering (Basel); Jan2025, Vol. 12 Issue 1, p17, 14p |
| Subject Terms: |
LANGUAGE models, CLINICAL decision support systems, ARTIFICIAL intelligence, INFORMATION retrieval, CLINICAL medicine |
| Abstract: |
Current clinical care relies heavily on complex, rule-based systems for tasks like diagnosis and treatment. However, these systems can be cumbersome and require constant updates. This study explores the potential of the large language model (LLM), LLaMA 2, to address these limitations. We tested LLaMA 2′s performance in interpreting complex clinical process models, such as Mayo Clinic Care Pathway Models (CPMs), and providing accurate clinical recommendations. LLM was trained on encoded pathways versions using DOT language, embedding them with SentenceTransformer, and then presented with hypothetical patient cases. We compared the token-level accuracy between LLM output and the ground truth by measuring both node and edge accuracy. LLaMA 2 accurately retrieved the diagnosis, suggested further evaluation, and delivered appropriate management steps, all based on the pathways. The average node accuracy across the different pathways was 0.91 (SD ± 0.045), while the average edge accuracy was 0.92 (SD ± 0.122). This study highlights the potential of LLMs for healthcare information retrieval, especially when relevant data are provided. Future research should focus on improving these models' interpretability and their integration into existing clinical workflows. [ABSTRACT FROM AUTHOR] |
|
Copyright of Bioengineering (Basel) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
