智慧医疗资讯个性化服务平台医学感知
Medical Perception of Intelligent Medical Information Personalized Service Platform

本文旨在研究一款智慧医疗资讯个性化服务平台——医学感知。传统的医疗资讯平台并不具备人工智能的相关算法帮助医生或者病人更准确、高效地获取医疗建议。本平台融合了三种不同的自然语言处理技术TF-IDF、Word2Vec、BERT等机器学习算法，通过实验比较出三者对于医疗信息匹配的不同特点从而为用户打造一个更加智能、更具人性化的医疗资讯服务平台。该平台基于Python语言及Django框架和MySQL数据库进行搭建，通过Requests和Beautiful Soup库实现对医疗数据的采集。
The purpose of this paper is to introduce a smart medical information personalized service platform—medical perception. The previous medical information platforms did not have artificial intelligence algorithms to help doctors or patients obtain medical advice more accurately and efficiently. The platform integrates three different natural language processing technologies, including TF-IDF, Word2Vec, and BERT machine learning algorithms. Through experiments, the differences in medical information matching characteristics of the three are compared in order to create a more intelligent and more personalized medical information service platform for users. This platform is built based on the Python language, Django framework, and MySQL database. It collects medical data through Requests and Beautiful Soup libraries.