A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes

• Main Authors: Chunlei Tang, Joseph M. Plasek, Haohan Zhang, Min-Jeoung Kang, Haokai Sheng, Yun Xiong, David W. Bates, Li Zhou
• Format: Article
• Language: English
• Published: BMC 2019-12-01
• Series: BMC Medical Informatics and Decision Making
• Subjects: “pulmonary disease; chronic obstructive,”; neural networks (computer); Disease progression; Data science
• Online Access: https://doi.org/10.1186/s12911-019-0984-8

Abstract Background Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients with COPD and to generate a temporal visualization that describes signs and symptoms during different stages of COPD progression. Methods We present a two-step approach for visualizing COPD progression at the level of unstructured clinical notes. We included 15,500 COPD patients who both received care within Partners Healthcare’s network and died between 2011 and 2017. We first propose a four-layer deep learning model that utilizes a specially configured recurrent neural network to capture irregular time lapse segments. Using those irregular time lapse segments, we created a temporal visualization (the COPD atlas) to demonstrate COPD progression, which consisted of representative sentences at each time window prior to death based on a fraction of theme words produced by a latent Dirichlet allocation model. We evaluated our approach on an annotated corpus of COPD patients’ unstructured pulmonary, radiology, and cardiology notes. Results Experiments compared to the baselines showed that our proposed approach improved interpretability as well as the accuracy of estimating COPD progression. Conclusions Our experiments demonstrated that the proposed deep-learning approach to handling temporal variation in COPD progression is feasible and can be used to generate a graphical representation of disease progression using information extracted from clinical notes.