{"title":"人工智能在急性肾损伤预测中的应用","authors":"Tushar Bajaj, Jay L. Koyner","doi":"10.1053/j.ackd.2022.07.009","DOIUrl":null,"url":null,"abstract":"<div><p><span>The use of artificial intelligence (AI) in nephrology and its associated </span>clinical research<span> is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.</span></p></div>","PeriodicalId":7221,"journal":{"name":"Advances in chronic kidney disease","volume":"29 5","pages":"Pages 450-460"},"PeriodicalIF":2.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Artificial Intelligence in Acute Kidney Injury Prediction\",\"authors\":\"Tushar Bajaj, Jay L. Koyner\",\"doi\":\"10.1053/j.ackd.2022.07.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>The use of artificial intelligence (AI) in nephrology and its associated </span>clinical research<span> is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.</span></p></div>\",\"PeriodicalId\":7221,\"journal\":{\"name\":\"Advances in chronic kidney disease\",\"volume\":\"29 5\",\"pages\":\"Pages 450-460\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in chronic kidney disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1548559522001367\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in chronic kidney disease","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1548559522001367","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
Artificial Intelligence in Acute Kidney Injury Prediction
The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.
期刊介绍:
The purpose of Advances Chronic Kidney Disease is to provide in-depth, scholarly review articles about the care and management of persons with early kidney disease and kidney failure, as well as those at risk for kidney disease. Emphasis is on articles related to the early identification of kidney disease; prevention or delay in progression of kidney disease