Causal inference and SHAP-based prediction models on real-world hospital data
Guangzhou Aid Cloud · 2020 – 2021
Working with clinicians at tertiary hospitals in China, I applied causal inference and survival analysis to real-world patient data, and designed a reusable interpretable machine-learning framework using SHAP for clinical prediction with transparent model explanations — including a predictor of acute kidney injury after liver transplantation (Zhang et al., 2021).
Background. Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. Methods. Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms. Results. 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model. Conclusions. Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.
@article{zhang2021aki,title={An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation},author={Zhang, Yihan and Yang, Dong and Liu, Zifeng and Chen, Chaojin and Ge, Mian and Li, Xiang and Luo, Tongsen and Wu, Zhengdong and Shi, Chenguang and Wang, Bohan and Huang, Xiaoshuai and Zhang, Xiaodong and Zhou, Shaoli and Hei, Ziqing},journal={Journal of Translational Medicine},volume={19},number={1},pages={321},year={2021},doi={10.1186/s12967-021-02990-4},}