Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information.

Guoxiang Xu,Hao Jin

doi:10.1155/2021/9637018

Abstract

With the continuous improvement of economic level and the continuous development of science and technology in China, information technology has begun to integrate into all walks of life. Medical units have begun to change from the traditional medical system to the intelligent system, and the processing of online case information has become an important component of medical informationization. To improve the efficiency of dealing with online case information, this study proposes to establish a fully connected neural network model to deal with online cases. Using jieba word segmentation tool and data preprocessing technology, the data of electronic medical records are sorted out, and the data are quantified using Word2Vec and other tools, and the data on electronic medical records are converted into one-hot binary variables. The quantified data are trained into a fully connected neural model, and the accuracy rate is about 88%. It is compared with naive Bayes and decision tree classification methods, and then a comparative experiment is carried out by solving e-health services in different ways. The results show that the fully connected neural network model has the best classification effect: the highest accuracy rate is about 93.7%, the highest precision rate is about 94.0%, the highest recall rate is about 95.3%, and the highest F1 score is about 94.6%. However, using artificial intelligence technology to solve electronic health services has great advantages, among which efficiency, assistance, and service satisfaction are all higher than 90%, which provides favorable technical support for electronic health services.

Highlights

Every country has some research on e-health services [1]. e United States introduced computers to assist calculation and introduced the development and promotion of electronic medical record system
The results show that artificial intelligence technology can effectively deal with online case information to solve the electronic health service. e second part of the study introduces the related knowledge of deep learning and data preprocessing technology. e third part analyzes the symptoms and diagnosis results in electronic medical records, and transforms and operates the data
Combined with the forecast and actual situation during classification, the correct rate, precision rate, recall rate, and F1 value are calculated by the following formulas: TP + TN Accuracy TP + FP + TN + FN, TP Precision TP + FP, (8)

Summary

Introduction

Every country has some research on e-health services [1]. e United States introduced computers to assist calculation and introduced the development and promotion of electronic medical record system. Britain has increased the research and development and promotion of electronic medical record system. Other countries are gradually developing their own electronic medical records to promote the informationization of medical services. In the research field of electronic medical records, machine learning technology has been used to mine and extract data from electronic medical records [2, 3]. Researchers conduct research on privacy removal of electronic medical records, patient status recognition, named entity recognition of electronic medical records, relationship extraction, etc., and organize relevant evaluation tasks to promote the development of related research [10]. In the evaluation task of named entity recognition in electronic medical records, the combination of conditional random fields and support vector machines has achieved good results, with 85.23%, 93.62%, and 73.13% in concept extraction, diagnosis detection, and relationship detection, respectively [11].

Methods

Results

Conclusion