Key result
The Self-explaining Hierarchical Model (SEHM) significantly outperformed baseline machine learning models in predicting postoperative complications, achieving an AUROC of 0.8086 for acute kidney injury while providing end-to-end interpretability.
p-value: p=<0.05
A novel self-explaining hierarchical model improves the accuracy and interpretability of predicting postoperative complications using intraoperative time series data.
Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivariate time series, prohibiting the effective learning of accurate models. The large gaps associated with clinical events and protocols are usually ignored. Moreover, deep models generally lack transparency. Nevertheless, the interpretability is crucial to assist clinicians in planning for and delivering postoperative care and timely interventions. Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series. We further develop an explanation module for the hierarchical model to interpret the predictions by providing contributions of intraoperative data in a fine-grained manner. Experiments on a large dataset of 111,888 surgeries with multiple outcomes and an external high-resolution ICU dataset show that our model can achieve strong predictive performance (i.e., high accuracy) and offer robust interpretations (i.e., high transparency) for predicted outcomes based on intraoperative time series.
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Li et al. (2022) studied Postoperative complications (delirium, pneumonia, acute kidney injury) (n=111,888). Self-explaining Hierarchical Model (SEHM) vs. Baseline machine learning models was evaluated on Area under the receiver operating characteristic curve (AUROC) for complication prediction (p=<0.05). The Self-explaining Hierarchical Model (SEHM) significantly outperformed baseline machine learning models in predicting postoperative complications, achieving an AUROC of 0.8086 for acute kidney injury while providing end-to-end interpretability.
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