Anis Davoudi, Ashkan Ebadi, Parisa Rashidi, Tazcan Ozrazgat-Baslanti, Azra Bihorac, Alberto C Bursian
{"title":"基于术前电子健康记录数据的机器学习模型预测谵妄。","authors":"Anis Davoudi, Ashkan Ebadi, Parisa Rashidi, Tazcan Ozrazgat-Baslanti, Azra Bihorac, Alberto C Bursian","doi":"10.1109/BIBE.2017.00014","DOIUrl":null,"url":null,"abstract":"<p><p>Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purposes. They additionally provide an efficient and inexpensive source of data for medical research, such as patient outcome prediction. In this study, we used preoperative Electronic Health Records to predict postoperative delirium. We compared the performance of seven machine learning models on delirium prediction: linear models, generalized additive models, random forests, support vector machine, neural networks, and extreme gradient boosting. Among the models evaluated in this study, random forests and generalized additive model outperformed the other models in terms of the overall performance metrics for prediction of delirium, particularly with respect to sensitivity. We found that age, alcohol or drug abuse, socioeconomic status, underlying medical issue, severity of medical problem, and attending surgeon can affect the risk of delirium.</p>","PeriodicalId":87347,"journal":{"name":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBE.2017.00014","citationCount":"37","resultStr":"{\"title\":\"Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data.\",\"authors\":\"Anis Davoudi, Ashkan Ebadi, Parisa Rashidi, Tazcan Ozrazgat-Baslanti, Azra Bihorac, Alberto C Bursian\",\"doi\":\"10.1109/BIBE.2017.00014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purposes. They additionally provide an efficient and inexpensive source of data for medical research, such as patient outcome prediction. In this study, we used preoperative Electronic Health Records to predict postoperative delirium. We compared the performance of seven machine learning models on delirium prediction: linear models, generalized additive models, random forests, support vector machine, neural networks, and extreme gradient boosting. Among the models evaluated in this study, random forests and generalized additive model outperformed the other models in terms of the overall performance metrics for prediction of delirium, particularly with respect to sensitivity. We found that age, alcohol or drug abuse, socioeconomic status, underlying medical issue, severity of medical problem, and attending surgeon can affect the risk of delirium.</p>\",\"PeriodicalId\":87347,\"journal\":{\"name\":\"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/BIBE.2017.00014\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2017.00014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2018/1/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2017.00014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/1/11 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data.
Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purposes. They additionally provide an efficient and inexpensive source of data for medical research, such as patient outcome prediction. In this study, we used preoperative Electronic Health Records to predict postoperative delirium. We compared the performance of seven machine learning models on delirium prediction: linear models, generalized additive models, random forests, support vector machine, neural networks, and extreme gradient boosting. Among the models evaluated in this study, random forests and generalized additive model outperformed the other models in terms of the overall performance metrics for prediction of delirium, particularly with respect to sensitivity. We found that age, alcohol or drug abuse, socioeconomic status, underlying medical issue, severity of medical problem, and attending surgeon can affect the risk of delirium.