Large Language Model-derived Symptom Clusters and Patient Outcomes in Colorectal Cancer from MIMIC-IV Clinical Notes
- 1University of Michigan
Abstract
BackgroundPrior research on symptom clusters (SCs) in colorectal cancer (CRC) has relied primarily on patient-reported outcome surveys, which capture symptom experience at discrete assessment points rather than the continuous documentation generated during routine care, leaving open whether SCs derived from electronic health record (EHR) text carry the same clinical meaning and predictive value. To construct and validate patient-level symptom co-occurrence networks from large language model (LLM)-extracted symptom data in CRC patients, and to test whether resulting SCs predict clinical outcomes.
MethodsUsing a zero-shot LLM extraction pipeline previously benchmarked against a manually annotated ground truth (Macro F1=0.70 for the best-performing model), we extracted 46 symptoms from 2,728 discharge notes of 1,507 CRC patients in MIMIC-IV. Patient-level symptom co-occurrence networks were constructed independently from Gemini 3.5 Flash and Claude Haiku extractions using phi correlation ([≥]0.10) and Louvain community detection, with sensitivity analyses across correlation thresholds, random seeds, note-aggregation strategy, and bootstrap resampling. Per-cluster symptom burden scores were tested as predictors of in-hospital mortality, 30-day readmission, and 1-year mortality using logistic regression adjusted for age, sex, and (in sensitivity models) metastatic disease.
ResultsBoth LLMs networks converged on three clinically coherent SCs -- Systemic, Gastrointestinal, and CRC Disease-Specific -- across sensitivity analyses (cross-model Adjusted Rand Index=0.727; 100-seed Louvain ARI=0.985; bootstrap ARI=0.733). The Systemic Symptom Cluster was the most consistent predictor of in-hospital mortality (OR=1.33) and 1-year mortality (OR=1.41), while the CRC Disease-Specific Cluster specifically and independently predicted 30-day readmission (OR=1.20); both associations were robust to adjustment for metastatic disease.
ConclusionLLM-extracted symptom data recover clinically coherent, reproducible SCs from unstructured discharge notes that carry independent prognostic value for mortality and readmission, supporting the clinical validity of automated, EHR-derived symptom profiling in CRC.
HighlightsO_LILLMs identified Systemic, CRC Disease-Specific, and Gastrointestinal clusters. C_LIO_LIThree symptom clusters were reproducible across LLMs and sensitivity analyses. C_LIO_LISystemic symptom burden predicted in-hospital and 1-year mortality. C_LI
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BibTeX
@misc{lee2026large,
title = {{Large Language Model-derived Symptom Clusters and Patient Outcomes in Colorectal Cancer from MIMIC-IV Clinical Notes}},
author = {Y. Lee and I. Dinov and X. Hu and Y. Jiang},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.15.26363148},
url = {https://www.medrxiv.org/content/10.64898/2026.09.15.26363148v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Large Language Model-derived Symptom Clusters and Patient Outcomes in Colorectal Cancer from MIMIC-IV Clinical Notes
AU - Y. Lee
AU - I. Dinov
AU - X. Hu
AU - Y. Jiang
PY - 2026
DA - 2026/09/15
PB - medRxiv
DO - 10.64898/2026.09.15.26363148
UR - https://www.medrxiv.org/content/10.64898/2026.09.15.26363148v1
AB - BackgroundPrior research on symptom clusters (SCs) in colorectal cancer (CRC) has relied primarily on patient-reported outcome surveys, which capture symptom experience at discrete assessment points rather than the continuous documentation generated during routine care, leaving open whether SCs derived from electronic health record (EHR) text carry the same clinical meaning and predictive value. To construct and validate patient-level symptom co-occurrence networks from large language model (LLM)-extracted symptom data in CRC patients, and to test whether resulting SCs predict clinical outcomes. MethodsUsing a zero-shot LLM extraction pipeline previously benchmarked against a manually annotated ground truth (Macro F1=0.70 for the best-performing model), we extracted 46 symptoms from 2,728 discharge notes of 1,507 CRC patients in MIMIC-IV. Patient-level symptom co-occurrence networks were constructed independently from Gemini 3.5 Flash and Claude Haiku extractions using phi correlation ([≥]0.10) and Louvain community detection, with sensitivity analyses across correlation thresholds, random seeds, note-aggregation strategy, and bootstrap resampling. Per-cluster symptom burden scores were tested as predictors of in-hospital mortality, 30-day readmission, and 1-year mortality using logistic regression adjusted for age, sex, and (in sensitivity models) metastatic disease. ResultsBoth LLMs networks converged on three clinically coherent SCs -- Systemic, Gastrointestinal, and CRC Disease-Specific -- across sensitivity analyses (cross-model Adjusted Rand Index=0.727; 100-seed Louvain ARI=0.985; bootstrap ARI=0.733). The Systemic Symptom Cluster was the most consistent predictor of in-hospital mortality (OR=1.33) and 1-year mortality (OR=1.41), while the CRC Disease-Specific Cluster specifically and independently predicted 30-day readmission (OR=1.20); both associations were robust to adjustment for metastatic disease. ConclusionLLM-extracted symptom data recover clinically coherent, reproducible SCs from unstructured discharge notes that carry independent prognostic value for mortality and readmission, supporting the clinical validity of automated, EHR-derived symptom profiling in CRC. HighlightsO_LILLMs identified Systemic, CRC Disease-Specific, and Gastrointestinal clusters. C_LIO_LIThree symptom clusters were reproducible across LLMs and sensitivity analyses. C_LIO_LISystemic symptom burden predicted in-hospital and 1-year mortality. C_LI
ER -