{"title":"基于规则集和机器学习的支持特发性膜性肾病自动风险分类和管理的专家系统","authors":"Dawid Pawuś , Szczepan Paszkiel , Tomasz Porażko","doi":"10.1016/j.bspc.2025.107989","DOIUrl":null,"url":null,"abstract":"<div><div>The diagnosis and management of idiopathic membranous nephropathy (IMN) is a complex clinical challenge due to the disease’s unpredictable progression and the varying responses to treatment. Traditional methods of risk stratification and treatment planning often rely on manual assessments, which can lead to inconsistent decision-making and suboptimal patient outcomes. To address this issue, we propose an expert system that leverages machine learning (ML) and artificial intelligence (AI) models and a knowledge-based approach to automate risk classification and treatment recommendations for IMN patients. This system aims to standardize and automate clinical decision-making, improve diagnostic accuracy, and enhance patient care through data-driven insights.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"109 ","pages":"Article 107989"},"PeriodicalIF":4.9000,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Expert system supporting automatic risk classification and management in idiopathic membranous nephropathy based on rule sets and machine learning\",\"authors\":\"Dawid Pawuś , Szczepan Paszkiel , Tomasz Porażko\",\"doi\":\"10.1016/j.bspc.2025.107989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The diagnosis and management of idiopathic membranous nephropathy (IMN) is a complex clinical challenge due to the disease’s unpredictable progression and the varying responses to treatment. Traditional methods of risk stratification and treatment planning often rely on manual assessments, which can lead to inconsistent decision-making and suboptimal patient outcomes. To address this issue, we propose an expert system that leverages machine learning (ML) and artificial intelligence (AI) models and a knowledge-based approach to automate risk classification and treatment recommendations for IMN patients. This system aims to standardize and automate clinical decision-making, improve diagnostic accuracy, and enhance patient care through data-driven insights.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":\"109 \",\"pages\":\"Article 107989\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-05-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809425005002\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809425005002","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Expert system supporting automatic risk classification and management in idiopathic membranous nephropathy based on rule sets and machine learning
The diagnosis and management of idiopathic membranous nephropathy (IMN) is a complex clinical challenge due to the disease’s unpredictable progression and the varying responses to treatment. Traditional methods of risk stratification and treatment planning often rely on manual assessments, which can lead to inconsistent decision-making and suboptimal patient outcomes. To address this issue, we propose an expert system that leverages machine learning (ML) and artificial intelligence (AI) models and a knowledge-based approach to automate risk classification and treatment recommendations for IMN patients. This system aims to standardize and automate clinical decision-making, improve diagnostic accuracy, and enhance patient care through data-driven insights.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.