基于Time-aware LSTM双向自动编码器的患者疾病分型

医学领域中, 患有相同疾病的患者之间也存在差异性, 看似简单的疾病也可能表现出不同程度的复杂性, 这给患者的识别、治疗和预后都带来巨大挑战. 本文使用以纵向非结构化时序存储的电子病历来解决患者异质性, 通过抓住就诊时间间隔不规律的特点增强对于隐藏信息的获取, 经过前向和后向的双向学习捕捉当前就诊记录与过去和未来信息的联系, 加深对于原序列特征提取的层次, 使模型做出更为精准的决策. 本文提出的BT-DST模型使用time-aware LSTM单元构造双向自动编码器学习患者强大的单一表示, 然后将其用于患者聚类, 通过统计分析得到患者针对当前疾病的亚型分型, 可针对不同群体采用不同类型的治疗干预, 为不同类患者提供针对其健康状况的精准医疗.

Patient Disease Typing Based on Time-aware LSTM Bidirectional Autoencoder

In the field of medicine, there are differences between patients with the same disease, and seemingly simple diseases may show different levels of complexity, which brings great challenges to patient identification, treatment, and prognosis. In this study, the electronic medical history stored in vertically unstructured time sequence is used to solve the heterogeneity of patients, enhance the acquisition of hidden information by seizing the characteristics of irregular medical treatment intervals, and capture the connection between current medical records and past and future information through forward and backward bidirectional learning, so as to deepen the level of feature extraction of original sequences and make the model make more accurate decisions. The BT-DST model proposed in this study?uses a time-aware LSTM unit to construct a bidirectional autoencoder to learn a strong single representation of a patient, which is then used in patient clustering to obtain the subtype of the patient for the current disease through statistical analysis. In addition, different types of therapeutic interventions can be applied to different populations, which provides precise medicine for different types of patients according to their health conditions.