Induparkavi Murugesan, K. Murugesan, Lingeshwaran Balasubramanian, Malathi Arumugam
{"title":"人工智能算法在脓毒症预测中的解释","authors":"Induparkavi Murugesan, K. Murugesan, Lingeshwaran Balasubramanian, Malathi Arumugam","doi":"10.23919/CinC49843.2019.9005667","DOIUrl":null,"url":null,"abstract":"Despite the rise of Artificial Intelligence (AI) algorithms and their applications in various fields, their utilizations in high-risk fields like healthcare and finance is limited because of the lack of interpretability of their inner workings. Some algorithms are interpretable, but not accurate, whereas some produce accurate results and not decipherable. Research is underway to explore the possibilities to interrogate an AI system, and ask why it makes certain decisions. This paper aims to investigate the decision-making process by AI algorithms in the prediction of sepsis based on patients’ clinical records.We were ranked 59 in the PhysioNet/Computing in Cardiology Challenge 2019 and the utility score obtained on the full test set is 0.131, and our team name was ARUL.","PeriodicalId":6697,"journal":{"name":"2019 Computing in Cardiology (CinC)","volume":"55 1","pages":"Page 1-Page 4"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Interpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis\",\"authors\":\"Induparkavi Murugesan, K. Murugesan, Lingeshwaran Balasubramanian, Malathi Arumugam\",\"doi\":\"10.23919/CinC49843.2019.9005667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite the rise of Artificial Intelligence (AI) algorithms and their applications in various fields, their utilizations in high-risk fields like healthcare and finance is limited because of the lack of interpretability of their inner workings. Some algorithms are interpretable, but not accurate, whereas some produce accurate results and not decipherable. Research is underway to explore the possibilities to interrogate an AI system, and ask why it makes certain decisions. This paper aims to investigate the decision-making process by AI algorithms in the prediction of sepsis based on patients’ clinical records.We were ranked 59 in the PhysioNet/Computing in Cardiology Challenge 2019 and the utility score obtained on the full test set is 0.131, and our team name was ARUL.\",\"PeriodicalId\":6697,\"journal\":{\"name\":\"2019 Computing in Cardiology (CinC)\",\"volume\":\"55 1\",\"pages\":\"Page 1-Page 4\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Computing in Cardiology (CinC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/CinC49843.2019.9005667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Computing in Cardiology (CinC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CinC49843.2019.9005667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
摘要
尽管人工智能(AI)算法的兴起及其在各个领域的应用,但由于其内部工作原理缺乏可解释性,它们在医疗保健和金融等高风险领域的应用受到限制。有些算法是可解释的,但不准确,而有些算法产生准确的结果,但不可破译。研究人员正在探索询问人工智能系统的可能性,并询问它为什么做出某些决定。本文旨在研究基于患者临床记录的AI算法在脓毒症预测中的决策过程。我们在2019年PhysioNet/Computing in Cardiology Challenge中排名第59位,在完整测试集上获得的效用得分为0.131,我们的团队名称为ARUL。
Interpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis
Despite the rise of Artificial Intelligence (AI) algorithms and their applications in various fields, their utilizations in high-risk fields like healthcare and finance is limited because of the lack of interpretability of their inner workings. Some algorithms are interpretable, but not accurate, whereas some produce accurate results and not decipherable. Research is underway to explore the possibilities to interrogate an AI system, and ask why it makes certain decisions. This paper aims to investigate the decision-making process by AI algorithms in the prediction of sepsis based on patients’ clinical records.We were ranked 59 in the PhysioNet/Computing in Cardiology Challenge 2019 and the utility score obtained on the full test set is 0.131, and our team name was ARUL.