Rupesh Raina, Kush Doshi, Pushan Aggarwal, Parker Kim, Jonathan Sasse, Sidharth Sethi, Carolyn Abitbol, Rolla Abu-Arja, Kianoush Kashani
{"title":"应用人工智能和机器学习对造血干细胞移植患者的急性肾损伤进行风险分层:PCRRT ICONIC人工智能倡议小组会议论文集。","authors":"Rupesh Raina, Kush Doshi, Pushan Aggarwal, Parker Kim, Jonathan Sasse, Sidharth Sethi, Carolyn Abitbol, Rolla Abu-Arja, Kianoush Kashani","doi":"10.5414/CN111421","DOIUrl":null,"url":null,"abstract":"<p><p>Acute kidney injury (AKI) is a frequent, severe complication of hematopoietic stem cell transplantation (HSCT) and is associated with an increased risk of morbidity and mortality. Recent advances in artificial intelligence (AI) and machine learning (ML) have showcased their proficiency in predicting AKI, projecting disease progression, and accurately identifying underlying etiologies. This review examines the central aspects of AKI post-HSCT, veno-occlusive disease (VOD) in HSCT recipients, discusses present-day applications of artificial intelligence in AKI, and introduces a proposed ML framework for the early detection of AKI risk.</p>","PeriodicalId":10396,"journal":{"name":"Clinical nephrology","volume":" ","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of artificial intelligence and machine learning for risk stratification acute kidney injury among hematopoietic stem cell transplantation patients: PCRRT ICONIC AI Initiative Group Meeting Proceedings.\",\"authors\":\"Rupesh Raina, Kush Doshi, Pushan Aggarwal, Parker Kim, Jonathan Sasse, Sidharth Sethi, Carolyn Abitbol, Rolla Abu-Arja, Kianoush Kashani\",\"doi\":\"10.5414/CN111421\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Acute kidney injury (AKI) is a frequent, severe complication of hematopoietic stem cell transplantation (HSCT) and is associated with an increased risk of morbidity and mortality. Recent advances in artificial intelligence (AI) and machine learning (ML) have showcased their proficiency in predicting AKI, projecting disease progression, and accurately identifying underlying etiologies. This review examines the central aspects of AKI post-HSCT, veno-occlusive disease (VOD) in HSCT recipients, discusses present-day applications of artificial intelligence in AKI, and introduces a proposed ML framework for the early detection of AKI risk.</p>\",\"PeriodicalId\":10396,\"journal\":{\"name\":\"Clinical nephrology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical nephrology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5414/CN111421\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical nephrology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5414/CN111421","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
急性肾损伤(AKI)是造血干细胞移植(HSCT)中一种常见的严重并发症,与发病率和死亡率的增加有关。人工智能(AI)和机器学习(ML)的最新进展展示了它们在预测 AKI、预测疾病进展和准确识别潜在病因方面的能力。这篇综述探讨了造血干细胞移植后 AKI 的核心问题、造血干细胞移植受者的静脉闭塞性疾病 (VOD),讨论了人工智能在 AKI 中的最新应用,并介绍了用于早期检测 AKI 风险的拟议 ML 框架。
Application of artificial intelligence and machine learning for risk stratification acute kidney injury among hematopoietic stem cell transplantation patients: PCRRT ICONIC AI Initiative Group Meeting Proceedings.
Acute kidney injury (AKI) is a frequent, severe complication of hematopoietic stem cell transplantation (HSCT) and is associated with an increased risk of morbidity and mortality. Recent advances in artificial intelligence (AI) and machine learning (ML) have showcased their proficiency in predicting AKI, projecting disease progression, and accurately identifying underlying etiologies. This review examines the central aspects of AKI post-HSCT, veno-occlusive disease (VOD) in HSCT recipients, discusses present-day applications of artificial intelligence in AKI, and introduces a proposed ML framework for the early detection of AKI risk.
期刊介绍:
Clinical Nephrology appears monthly and publishes manuscripts containing original material with emphasis on the following topics: prophylaxis, pathophysiology, immunology, diagnosis, therapy, experimental approaches and dialysis and transplantation.