We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. To analyze if these representations are transferable across tasks, we evaluate them in multiple supervised setups to predict patient mortality, primary diagnostic and procedural category, and gender. We compare their performance with sparse representations obtained from a bag-of-words model. We observe that the learned generalized representations significantly outperform the sparse representations when we have few positive instances to learn from, and there is an absence of strong lexical features. 2. We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts. In the latter case, concepts represent problems, treatments, and tests. We find that concept identification does not improve the classification performance. 3. We propose novel techniques to facilitate model interpretability. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate feature sensitivity across two networks to identify the most significant input features for different classification tasks when we use these pretrained representations as the supervised input. We successfully extract the most influential features for the pipeline using this technique.

该研究通过分别使用堆叠去噪自编码器和段落向量模型来学习临床记录中与任务独立的密集患者表示。进而比较疾病分类等监督学习任务中，密集表示相较于传统的词袋模型的表现以及从医学概念中提取特征的表现。结果表明，密集表示在处理少量数据和缺少强语义信息的任务时有优势。最后，提出的新技术有助于强化模型可解释性。

临床记录患者表征学习及其可解释性评估