Improving the Prediction of Unplanned 30-day Cancer Readmissions Using Social Determinants of Health: A Geocoding-based Approach

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Abstract
Unplanned cancer readmissions present a significant burden on patients and hospitals. Current predictive models often overlook socioeconomic factors such as social determinants of health (SDoH), which have the potential to improve prediction performance, as measured by the Area Under the Receiver Operating Characteristic (AUROC) and the Precision-Recall Curve (AUPRC). To investigate this, the present study developed predictive models using cancer readmission data from a large health system in Hamilton County, OH. The models incorporated geocoding-based SDoH along with clustering techniques and compared machine learning (ML) and deep learning (DL) algorithms. Overall, models, regardless of algorithm type, not using SDoH variables had higher AUROC and AUPRCs. The best-performing ML and DL models are comparable (AUROC = 0.7605 for ML; AUROC = 0.7585 for DL). However, when top-performing models were evaluated across certain organ and system cancers, using SDoH and clustering techniques significantly improved model performance. This was most notable for cancers of the skin, subcutaneous tissue, and breast with improvements of 8.20% in AUROC and 11.04% in AUPRC. For all cancer patient cases, utilizing individualized SDoH information extracted from clinical notes was recommended for future studies.

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