Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges  

作　　者：Ebenezer Afrifa-Yamoah Eric Adua Emmanuel Peprah-Yamoah Enoch O.Anto Victor Opoku-Yamoah Emmanuel Acheampong Michael J.Macartney Rashid Hashmi 

机构地区：[1]School of Science,Edith Cowan University,Joondalup,Western Australia,Australia [2]Rural Clinical School,Medicine and Health,University of New South Wales,Sydney,New South Wales,Australia [3]School of Medical and Health Sciences,Edith Cowan University,Joondalup,Western Australia,Australia [4]Teva Pharmaceuticals,Salt Lake City,Utah,USA [5]Department of Medical Diagnostics,Faculty of Allied Health Sciences,College of Health Sciences,Kwame Nkrumah University of Science and Technology,Kumasi,Ghana [6]School of Optometry and Vision Science,University of Waterloo,Waterloo,Ontario,Canada [7]Department of Genetics and Genome Biology,Leicester Cancer Research Centre,University of Leicester,Leicester,UK [8]Faculty of Science Medicine and Health,University of Wollongong,Wollongong,New South Wales,Australia

出　　处：《Chronic Diseases and Translational Medicine》2025年第1期1-21,共21页慢性疾病与转化医学(英文版)

摘　　要：Chronic diseases such as heart disease,cancer,and diabetes are leading drivers of mortality worldwide,underscoring the need for improved efforts around early detection and prediction.The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics,transcriptomics,proteomics,glycomics,and lipidomics.The complex biomarker and mechanistic data from these"omics"studies present analytical and interpretive challenges,especially for traditional statistical methods.Machine learning(ML)techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis.This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets,including medical imaging,genomics,wearables,and electronic health records.Specifically,we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures.We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field.While highlighting the critical innovations and successes emerging in this space,we identify the key challenges and limitations that remain to be addressed.Finally,we discuss pathways forward toward scalable,equitable,and clinically implementable ML solutions for transforming chronic disease screening and prevention.

关 键 词：big data chronic diseases disease prediction machine learning algorithms OMICs data 

分 类 号：R-05[医药卫生]