Toward Transparent Sequence Models with Model-Based Tree Markov Model
This addresses interpretability for healthcare applications like ICU mortality prediction, but it is incremental as it combines existing methods (MOB trees and HSMM) with DNN knowledge transfer.
The study tackled interpretability in black-box sequence models by introducing the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM) for detecting high mortality risk events in ICU data, leveraging knowledge from DNNs to improve predictive performance while providing clear explanations.
In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information.