In IEEE/ACM transactions on computational biology and bioinformatics
The rapidly developed Health 2.0 technology has provide people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical activities becomes a significant issue to facilitate patients' medical decision making process. As a subcategory of machine learning, neural networks have drawn increasing attentions in natural language processing applications. In this study, we focus on the modeling and analysis of patient-physician-generated data based on an integrated CNN-RNN framework. A CNN-based classifier is designed to extract textural features in the sentence-level, and a RNN-based model is constructed to learn the potential patterns and correlations in a dialog-level. An intelligent recommendation mechanism is then developed to provide patients with automatic clinic guide and pre-diagnosis suggestions in a data-driven way. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for online medical pre-diagnosis support.
Zhou Xiaokang, Li Yue, Liang Wei