Development and Validation of a Machine Learning Approach Leveraging Real-World Clinical Narratives as a Predictor of Survival in Advanced Cancer

Abstract

PURPOSE: Predicting short-term mortality in patients with advanced cancer remains challenging. Whether digitalized clinical text can be used to build models to enhance survival prediction in this population is unclear. MATERIALS AND METHODS: We conducted a single-centered retrospective cohort study in patients with advanced solid tumors. Clinical correspondence authored by oncologists at the first patient encounter was extracted from the electronic medical records. Machine learning (ML) models were trained using narratives from the derivation cohort, before being tested on a temporal validation cohort at the same site. Performance was benchmarked against Eastern Cooperative Oncology Group performance status (PS), comparing ML models alone (comparison 1) or in combination with PS (comparison 2), assessed by areas under receiver operating characteristic curves (AUCs) for predicting vital status at 11 time points from 2 to 52 weeks. RESULTS: .001, comparison 2); the AUC was > 0.80 at all assessed time points for models incorporating clinical text. Exploratory analysis of oncologist's narratives revealed recurring descriptors correlating with survival, including referral patterns, mobility, physical functions, and concomitant medications. CONCLUSION: Applying ML to oncologists' narratives with or without including patient's PS significantly improved survival prediction to 12 months, suggesting the utility of clinical text in building prognostic support tools.

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