C-LIME: A Consistency-Oriented LIME for Time-Series Health-Risk Predictions

Predicting health risk from electronic health records (EHRs) is increasingly demanded in the medical and health fields. Many studies have pursued prediction accuracy while ignoring the interpretability of their developed models. To encourage lifestyle changes by patients and employees, an appropriate explanation of why the model outputs high risk is as important as accurately predicting the health risk. In this study, we construct 33 predictive models (11 health-checkup items checked after one, two, and three years). We also clarify a problem in the existing Local Interpretable Model-agnostic Explanations (LIME), namely, inconsistency among the health-risk predictions of the three target years. To resolve this problem, we find and exclude an anomalous sample that deteriorate the interpretation, and output a consistent interpretation of the health-risk predictions over the three years. We evaluate proposed method using more than 10,000 medical examination data. Accuracy was improved by 16% at the maximum compared to the baseline that output the risk at year Y + 1,2,3 equaling to that at year Y. Also, proposed LIME called C-LIME improve number of employees whom we can provide consistent lifestyle advice over the years three times compared to LIME. We have released a health-risk prediction and lifestyle recommendation service using proposed method for employees of the Nippon Telegraph and Telephone Group from April of 2019.