Explainable Tree-Based Predictions for Unplanned 30-Day Readmission of Patients With Cancer Using Clinical Embeddings

Chi Wah Wong, Chen Chen, Lorenzo A. Rossi, Monga Abila

Abstract:

PURPOSE Thirty-day unplanned readmission is one of the key components in measuring quality in patient care.
Risk of readmission in oncology patients may be associated with a wide variety of specific factors including
laboratory results and diagnoses, and it is hard to include all such features using traditional approaches such as
one-hot encoding in predictive models.
METHODS We used clinical embeddings to represent complex medical concepts in lower dimensional spaces.
For predictive modeling, we used gradient-boosted trees and adopted the shapley additive explanation
framework to offer consistent individualized predictions. We used retrospective inpatient data between 2013 and
2018 with temporal split for training and testing.
RESULTS Our best performing model predicting readmission at discharge using clinical embeddings showed a
testing area under receiver operating characteristic curve of 0.78 (95% CI, 0.77 to 0.80). Use of clinical
embeddings led to up to 23.1% gain in area under precision-recall curve and 6% in area under receiver
operating characteristic curve. Hematology models had more performance gain over surgery and medical
oncology. Our study was the first to develop (1) explainable predictive models for the hematology population and
(2) dynamic models to keep track of readmission risk throughout the duration of patient visit.
CONCLUSION To our knowledge, our study was the first to develop (1) explainable predictive models for the hematology population and (2) dynamic models to keep track of readmission risk throughout the duration of patient visit.