An intelligent medical guidance and recommendation model driven by patient-physician communication data

Jusheng Liu; Chaoran Li; Ye Huang; Jingti Han

doi:10.3389/fpubh.2023.1098206

An intelligent medical guidance and recommendation model driven by patient-physician communication data

Front Public Health. 2023 Jan 26:11:1098206. doi: 10.3389/fpubh.2023.1098206. eCollection 2023.

Authors

Jusheng Liu¹, Chaoran Li², Ye Huang^{3

4}, Jingti Han^{3

4}

Affiliations

¹ School of Economics and Management, Shanghai University of Political Science and Law, Shanghai, China.
² School of Economics and Management, Shanghai University of Sport, Shanghai, China.
³ School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, China.
⁴ Shanghai Financial Intelligent Engineering Technology Research Center, Shanghai, China.

Abstract

Based on the online patient-physician communication data, this study used natural language processing and machine learning algorithm to construct a medical intelligent guidance and recommendation model. First, based on 16,935 patient main complaint data of nine diseases, this study used the word2vec, long-term and short-term memory neural networks, and other machine learning algorithms to construct intelligent department guidance and recommendation model. Besides, taking ophthalmology as an example, it also used the word2vec, TF-IDF, and cosine similarity algorithm to construct an intelligent physician recommendation model. Furthermore, to recommend physicians with better service quality, this study introduced the information amount of physicians' feedback to the recommendation evaluation indicator as the text and voice service quality. The results show that the department guidance model constructed by long-term and short-term memory neural networks has the best effect. The precision is 82.84%, and the F1-score is 82.61% in the test set. The prediction effect of the LSTM model is better than TextCNN, random forest, K-nearest neighbor, and support vector machine algorithms. In the intelligent physician recommendation model, under certain parameter settings, the recommendation effect of the hybrid recommendation model based on similar patients and similar physicians has certain advantages over the model of similar patients and similar physicians.

Keywords: healthcare; natural language processing; patient-physician communication data; recommendation system; text analytics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Communication
Humans
Machine Learning
Neural Networks, Computer*
Physicians*

Grants and funding

This work was supported by the Major Program of the National Fund of Philosophy and Social Science of China (Grant No. 18ZDA088), the Shanghai Engineering Research Center of Finance Intelligence (Grant No. 19DZ2254600), the National Natural Science Foundation Project (Grant No. 71871144), the Shanghai Social Science Planning Youth Project (Grant No. 2019EXW001), the Shanghai University of Sport (Grant No. 2022XJ024), and the Shanghai Universities Young Teacher Training Funding Program.