Faculty, Staff and Student Publications
Language
English
Publication Date
1-1-2024
Journal
AMIA 2026 Annual Symposium Proceedings
PMID
40417538
PMCID
PMC12099403
PubMedCentral® Posted Date
5-22-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Cancer treatments are known to introduce cardiotoxicity, negatively impacting outcomes and survivorship. Identifying cancer patients at risk of heart failure (HF) is critical to improving cancer treatment outcomes and safety. This study examined machine learning (ML) models to identify cancer patients at risk of HF using electronic health records (EHRs), including traditional ML, Time-Aware long short-term memory (T-LSTM), and large language models (LLMs) using novel narrative features derived from the structured medical codes. We identified a cancer cohort of 12,806 patients from the University of Florida Health, diagnosed with lung, breast, and colorectal cancers, among which 1,602 individuals developed HF after cancer. The LLM, GatorTron-3.9B, achieved the best F1 scores, outperforming the traditional support vector machines by 39%, the T-LSTM deep learning model by 7%, and a widely used transformer model, BERT, by 5.6%. The analysis shows that the proposed narrative features remarkably increased feature density and improved performance.
Keywords
Humans, Heart Failure, Electronic Health Records, Neoplasms, Machine Learning, Natural Language Processing, Risk Assessment, Narration, Female, Large Language Models
Published Open-Access
yes
Recommended Citation
Chen, Ziyi; Zhang, Mengyuan; Ahmed, Mustafa Mohammed; et al., "Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure" (2024). Faculty, Staff and Student Publications. 937.
https://digitalcommons.library.tmc.edu/uthshis_docs/937