{"title":"概念漂移检测和自适应框架在心脏手术非线性时间序列数据中的应用","authors":"Rajarajan Ganesan, Tarunpreet Kaur, Alisha Mittal, Mansi Sahi, Sushant Konar, Tanvir Samra, Goverdhan Dutt Puri, Shayam Kumar Singh Thingnum, Nitin Auluck","doi":"10.1111/coin.12658","DOIUrl":null,"url":null,"abstract":"<p>The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery\",\"authors\":\"Rajarajan Ganesan, Tarunpreet Kaur, Alisha Mittal, Mansi Sahi, Sushant Konar, Tanvir Samra, Goverdhan Dutt Puri, Shayam Kumar Singh Thingnum, Nitin Auluck\",\"doi\":\"10.1111/coin.12658\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.</p>\",\"PeriodicalId\":55228,\"journal\":{\"name\":\"Computational Intelligence\",\"volume\":\"40 3\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/coin.12658\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/coin.12658","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
由于用于训练的数据与即将用于预测的数据之间存在差异,在动态环境中部署的机器学习(ML)模型的质量往往会随着时间的推移而下降,这就是通常所说的漂移。因此,ML 模型必须能够检测数据分布中的任何变化或漂移,并相应地更新 ML 模型。本研究介绍了各种漂移检测技术,以识别心脏手术患者生存结果中的漂移。此外,本研究还提出了几种漂移适应策略,如自适应学习、增量学习和集合学习。通过对结果的详细分析,研究证实了集合模型的卓越性能,在预测住院时间和重症监护室住院时间方面,集合模型的最小平均绝对误差(MAE)分别为 10.684 和 2.827。此外,与不包含漂移自适应框架的模型相比,包含漂移自适应框架的模型表现出更优越的性能。
Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery
The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.
期刊介绍:
This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.