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Tuesday, October 26 • 2:00pm - 2:15pm
Explainable Deep Learning Approaches to Predict Development of Brain Metastases in Patients with Lung Cancer Using Electronic Health Records

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Technical Presentations Group 4: Healthcare

Brain metastases (BM) from lung cancer accounts for the majority of BM cases. Brain metastases cause neurological morbidity and affect quality of life as they can be associated with brain edema. Therefore, early detection of brain metastases and prompt treatment can achieve optimal control. In this study, we employed the RNN-based RETAIN model to predict the risk of developing BM among patients diagnosed with lung cancer based on electronic health record (EHR) data. Meanwhile, we also extended the feature attribution method, Kernel SHAP, to structural EHR data to interpret the decision process. The deep learning models utilize the longitudinal information between different patient encounters to obtain explainable predictions for BM. Through a series of well-defined cohort construction and case-control matching criteria, the best AUC in the test set was obtained by RETAIN reaching 0.825, which achieved 3.7% improvement compared with the baseline model. The high contribution list identified by RETAIN and Kernel EHR was highly related to BM development, and especially to the higher lung cancer stages. Moreover, the sensitivity analysis also demonstrated that both RETAIN and Kernel SHAP can recognize the unrelated features and put more contribution to the important features.

Authors: Zhao Li (UTHealth), Ping Zhu (UTHealth), Rongbin Li (UTHealth), Yoshua Esquenazi (UTHealth) and W. Jim Zheng (UTHealth)

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Tuesday October 26, 2021 2:00pm - 2:15pm CDT

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