{"title":"急性并发症预测和诊断模型 CLSTM-BPR:时间序列深度学习与贝叶斯个性化排名的融合方法","authors":"Xi Chen;Quan Cheng","doi":"10.26599/TST.2023.9010103","DOIUrl":null,"url":null,"abstract":"Acute complication prediction model is of great importance for the overall reduction of premature death in chronic diseases. The CLSTM-BPR proposed in this paper aims to improve the accuracy, interpretability, and generalizability of the existing disease prediction models. Firstly, through its complex neural network structure, CLSTM-BPR considers both disease commonality and patient characteristics in the prediction process. Secondly, by splicing the time series prediction algorithm and classifier, the judgment basis is given along with the prediction results. Finally, this model introduces the pairwise algorithm Bayesian Personalized Ranking (BPR) into the medical field for the first time, and achieves a good result in the diagnosis of six acute complications. Experiments on the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset show that the average Mean Absolute Error (MAE) of biomarker value prediction of the CLSTM-BPR model is 0.26, and the average accuracy (ACC) of the CLSTM-BPR model for acute complication diagnosis is 92.5%. Comparison experiments and ablation experiments further demonstrate the reliability of CLSTM-BPR in the prediction of acute complication, which is an advancement of current disease prediction tools.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":null,"pages":null},"PeriodicalIF":6.6000,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517923","citationCount":"0","resultStr":"{\"title\":\"Acute Complication Prediction and Diagnosis Model CLSTM-BPR: A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking\",\"authors\":\"Xi Chen;Quan Cheng\",\"doi\":\"10.26599/TST.2023.9010103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Acute complication prediction model is of great importance for the overall reduction of premature death in chronic diseases. The CLSTM-BPR proposed in this paper aims to improve the accuracy, interpretability, and generalizability of the existing disease prediction models. Firstly, through its complex neural network structure, CLSTM-BPR considers both disease commonality and patient characteristics in the prediction process. Secondly, by splicing the time series prediction algorithm and classifier, the judgment basis is given along with the prediction results. Finally, this model introduces the pairwise algorithm Bayesian Personalized Ranking (BPR) into the medical field for the first time, and achieves a good result in the diagnosis of six acute complications. Experiments on the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset show that the average Mean Absolute Error (MAE) of biomarker value prediction of the CLSTM-BPR model is 0.26, and the average accuracy (ACC) of the CLSTM-BPR model for acute complication diagnosis is 92.5%. Comparison experiments and ablation experiments further demonstrate the reliability of CLSTM-BPR in the prediction of acute complication, which is an advancement of current disease prediction tools.\",\"PeriodicalId\":48690,\"journal\":{\"name\":\"Tsinghua Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2024-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517923\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tsinghua Science and Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10517923/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Multidisciplinary\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tsinghua Science and Technology","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10517923/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Multidisciplinary","Score":null,"Total":0}
Acute Complication Prediction and Diagnosis Model CLSTM-BPR: A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking
Acute complication prediction model is of great importance for the overall reduction of premature death in chronic diseases. The CLSTM-BPR proposed in this paper aims to improve the accuracy, interpretability, and generalizability of the existing disease prediction models. Firstly, through its complex neural network structure, CLSTM-BPR considers both disease commonality and patient characteristics in the prediction process. Secondly, by splicing the time series prediction algorithm and classifier, the judgment basis is given along with the prediction results. Finally, this model introduces the pairwise algorithm Bayesian Personalized Ranking (BPR) into the medical field for the first time, and achieves a good result in the diagnosis of six acute complications. Experiments on the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset show that the average Mean Absolute Error (MAE) of biomarker value prediction of the CLSTM-BPR model is 0.26, and the average accuracy (ACC) of the CLSTM-BPR model for acute complication diagnosis is 92.5%. Comparison experiments and ablation experiments further demonstrate the reliability of CLSTM-BPR in the prediction of acute complication, which is an advancement of current disease prediction tools.
期刊介绍:
Tsinghua Science and Technology (Tsinghua Sci Technol) started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. Contributions all over the world are welcome.