KTI-RNN:Recognition of Heart Failure from Clinical Notes

作     者：Dengao Li Huiting Ma Wenjing Li Baofeng Zhao Jumin Zhao Yi Liu Jian Fu Dengao Li;Huiting Ma;Wenjing Li;Baofeng Zhao;Jumin Zhao;Yi Liu;Jian Fu

作者机构：College of Data ScienceTaiyuan University of TechnologyJinzhong 030600China Department of Statistics and Applied ProbabilityUniversity of CaliforniaSanta BarbaraCA 93106USA College of Mining EngineeringTaiyuan University of TechnologyTaiyuan 030024China College of Information and ComputerTaiyuan University of TechnologyJinzhong 030600China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2023年第28卷第1期

页      面：117-130页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1205[管理学-图书情报与档案管理] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Major Scientific Research Instrument Development Project (No.62027819):High-Speed Real-Time Analyzer for Laser Chip’s Optical Catastrophic Damage Process the General Object of the National Natural Science Foundation (No.62076177):Study on the Risk Assessment Model of Heart Failure by Integrating Multi-Modal Big Data Shanxi Province Key Technology and Generic Technology R&D Project (No.2020XXX007):Energy Internet Integrated Intelligent Data Management and Decision Support Platform. 

主　　题：heart failure diagnosis text classification deep learning 

摘      要：Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult task.In particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions.This study uses data from the Medical Information Mart for Intensive Care database.Compared with structured data,unstructured data contain abundant patient information.However,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse content.To solve these problems,we propose the KTI-RNN model for unstructured data recognition.The proposed model overcomes sparse content and obtains good classification results.The term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword set.The latent dirichlet allocation(LDA)model is adopted to extract the topic word set.These models enable the expansion of the medical record text content.Finally,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output layer.We call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical texts.Results show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.