CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support

TLDR

Health 2.0 has expanded online medical consultations, making context understanding in patient–physician interactions crucial, and neural networks are increasingly applied in NLP for this purpose. The study aims to model and analyze patient–physician data with an integrated CNN–RNN framework to address the brevity of online inquiries. A DP‑CRNN algorithm with a novel neural architecture extracts combined semantic and sequential features from inquiries, and an intelligent recommendation system uses clustering to refine learning and provide precise clinic guidance and pre‑diagnosis suggestions. Experiments on real‑world data confirm that the proposed model and recommendation method effectively support intelligent pre‑diagnosis in online medical settings.

Abstract

The rapidly developed Health 2.0 technology has provided people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical communications and 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 article, we focus on modeling and analyzing the patient-physician-generated data based on an integrated CNN-RNN framework, in order to deal with the situation that patients' online inquiries are usually not very long. A so-called DP-CRNN algorithm is developed with a newly designed neural network structure, to extract and highlight the combination of semantic and sequential features in terms of patient's inquiries. An intelligent recommendation method is then proposed to provide patients with automatic clinic guidance and pre-diagnosis suggestions, in which a clustering mechanism is utilized to refine the learning process with more precise diagnosis scope and more representative features. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for intelligent pre-diagnosis service in online medical environments.