With the increasing availability of patients' data, modern medicine is shifting towards prospective healthcare. Electronic health records contain a variety of information useful for clinical patient description and can be exploited for the construction of predictive models, given that similar medical histories will likely lead to similar progressions. One example is unplanned hospital readmission prediction, an essential task for reducing hospital costs and improving patient health. Despite predictive models showing very good performances especially with deep-learning models, they are often criticized for the poor interpretability of their results, a fundamental characteristic in the medical field, where incorrect predictions might have serious consequences for the patient health. In this paper we propose a novel, interpretable deep-learning framework for predicting unplanned hospital readmissions, supported by NLP findings on word embeddings and by neural-network models (ConvLSTM) for better handling temporal data. We validate our system on the two predictive tasks of hospital readmission within 30 and 180 days, using real-world data. In addition, we introduce and test a model-dependent technique to make the representation of results easily interpretable by the medical staff. Our solution achieves better performances compared to traditional models based on machine learning, while providing at the same time more interpretable results.

翻译：随着患者数据的日益丰富，现代医学正逐步向预防性医疗转变。电子健康记录包含用于临床患者描述的多维信息，鉴于相似病史可能导致相似病程进展，这些信息可用于构建预测模型。其中一项重要任务是非计划性医院再入院预测，这对降低医疗成本、改善患者健康至关重要。尽管预测模型（尤其是深度学习模型）展现出优异性能，但其结果可解释性不足常遭诟病——在医学领域，错误预测可能对患者健康造成严重后果。本文提出一种新型可解释深度学习框架，用于预测非计划性医院再入院。该框架基于词嵌入的自然语言处理研究成果，并采用神经网络模型（ConvLSTM）以更优方式处理时序数据。我们利用真实世界数据，在30天和180天医院再入院两个预测任务中验证了系统性能。此外，我们引入并测试了一种模型依赖技术，使医疗人员能够轻松解释结果表征。相较于传统机器学习模型，本方案在提供更优性能的同时，实现了更具可解释性的预测结果。