Leveraging Large Language Models and Machine Learning for Success Analysis in Robust Cancer Crowdfunding Predictions: Quantitative Study

作者：Runa Bhaumik, Abhishikta Roy, Vineet Srivastava, Lokesh Boggavarapu, C. Ranganathan, Edward Mensah, John P. Galvin · 发表于：JMIR AI · 年份：2025 · DOI：10.2196/73448 · 研究领域：FinTech, Crowdfunding, Digital Finance、Misinformation and Its Impacts、Artificial Intelligence in Healthcare and Education

BACKGROUND: Recent advances in large language models (LLMs), such as GPT-4o, offer a transformative opportunity to extract nuanced linguistic, emotional, and social features from campaign texts at scale. These models enable a deeper understanding of the factors influencing campaign success-far beyond what structured data alone can reveal. Given these advancements, there is a pressing need for an integrated modeling framework that leverages both LLM-derived features and machine learning algorithms to more accurately predict and explain success in medical crowdfunding. OBJECTIVE: This study addresses that gap by leveraging cutting-edge machine learning techniques alongside state-of-the-art large language models such as GPT-4o to automatically generate and extract nuanced linguistic, social, and clinical features from campaign narratives. By combining these features with ensemble learning approaches, the proposed methodology offers a novel and more comprehensive strategy for understanding and predicting crowdfunding success in the medical domain. METHODS: We used GPT-4o to extract linguistic and social determinants of health (SDOH) features from cancer crowdfunding campaign narratives. A Random Forest model with permutation importance was applied to rank features based on their contribution to predicting campaign success. Four machine learning algorithms-Random Forest, Gradient Boosting, Logistic Regression, and Elastic Net-were evaluated using stratified 10-fold cross-validatio...