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.

翻译：在本研究中，我们旨在解决应用于序列数据的复杂黑箱机器学习模型的可解释性问题。我们提出了基于模型的树状隐半马尔可夫模型（MOB-HSMM），这是一种内在可解释的模型，用于检测重症监护病房（ICU）中的高死亡风险事件，并发现与死亡风险相关的隐藏模式。该模型利用从深度神经网络（DNN）中提取的知识来提升预测性能，同时提供清晰的解释。实验结果表明，通过使用LSTM学习序列模式并将其迁移至基于模型的树（MOB树），模型性能得到了提升。将MOB树与隐半马尔可夫模型（HSMM）集成至MOB-HSMM中，能够利用现有信息揭示潜在且可解释的序列模式。