Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Year: 2024
Type: conference-paper
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.
Source: PubMed
Authors: Chen, Ziyi, Zhang, Mengyuan, Ahmed, Mustafa Mohammed, Guo, Yi, George, Thomas J., Bian, Jiang, Wu, Yonghui
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: Patient-Centered Outcomes Research Institute, Nvidia, National Institutes of Health, University of Florida Health, Clinical and Translational Science Institute, University of Florida, National Institute on Aging, National Cancer Institute, National Institute of Allergy and Infectious Diseases
Awards: G1254130036, Advancing Drug Repositioning for Alzheimer’s Disease using Real-world Data, G1590729938, G1829463717, Post-Acute Sequelae of SARS-CoV-2 Infection and Subsequent Disease Progression in Individuals with AD/ADRD: Influence of the Social and Environmental Determinants of Health, G2654747551, G3398941559, G4156463097, G4691473461, G4696575845, The benefits and harms of lung cancer screening in Florida, Using Electronic Health Records from a Large Clinical Data Research Network to Understand Cancer Burden and Cancer Risks Among Transgender and Gender Nonconforming (TGNC) Individuals, Advancing Interdisciplinary Science of Aging through Identification of Iatrogenic Complications: The UF EHR Clinical Data Infrastructure for Enhanced Patient Safety among the Elderly (UF-ECLIPSE), G7257327873, G7884167642, G8156115585, G8516541333, G8743902842, An end-to-end informatics framework to study Multiple Chronic Conditions (MCC)'s impact on Alzheimer's disease using harmonized electronic health records, G982581970
OpenAlex ID: W4393055550