[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117567-en":3,"doc-seo-117567-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117567,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Review of Interpretable Machine Learning Models for Disease Prognosis","Interpretable machine learning has attracted major attention during the COVID-19 pandemic because it enables transparent, understandable predictive insights needed for clinical decision-making. This literature review surveys interpretable approaches applied to prognosis prediction for respiratory diseases, with emphasis on COVID-19. It highlights models that integrate existing clinical domain knowledge while also learning new signals from data, supporting both crisis management and future outbreak readiness. By improving preparedness and response, interpretable systems can strengthen patient outcomes and reduce respiratory-disease morbidity and mortality.","Review of Interpretable Machine Learning Models for  \nDisease Prognosis  \nJinzhi Shen  \nDepartment of Computer and Electrical Engineering Boston University Boston, MA, USA [jinzhis9@bu.edu](jinzhis9@bu.edu)  \nKe Ma Department of Economics University of California, Santa Cruz Santa Cruz, CA 95064 [kma41@ucsc.edu](kma41@ucsc.edu)  \nAbstract— In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decisionmaking. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. Wereviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomesand mitigating the impact of respiratory diseases in the years to come.  \nKeywords— Interpretable machine learning, Domain knowledge, Respiratory diseases, COVID-19, Prognosis  \nI. INTRODUCTION  \nThe integration of interpretable machine learning (IML) techniques into healthcare has garnered substantial attention [1]–[8] . The COVID-19 pandemic has underscored the urgent need for transparent and understandable predictive models [1],[3], [7]–[11] . This literature review aims to explore the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, with a specific focus on COVID-19 and its implications for future research and clinical practice.  \nThe COVID-19 pandemic has underscored the urgency of developing accurate prognostic tools to identify high-risk individuals and allocate healthcare resources effectively. In response, numerous interpretable machine learning models have been developed to predict COVID-19 severity [12], [13], mortality risk [14]–[17], and complications [18] . These models not only enhance our understanding of the disease but also offer valuable insights into the broader landscape of respiratory diseases.  \nMoreover, the experiences gained from applying interpretable machine learning in the context ofCOVID-19 are poised to have far-reaching implications for future disease  \noutbreaks [19], [20] . By harnessing interpretable machine learning, healthcare systems can improve their preparedness and response capabilities, leading to better patient outcomes and reduced morbidity and mortality from respiratory diseases.  \nThis review will examine the current state of research on interpretable machine learning for respiratory disease prognosis, with a focus on COVID-19. It will explore the various methodologies and applications of interpretable machine learning in this domain, highlighting their strengths, limitations, and potential impact on clinical practice. Additionally, it will discuss future directions and challenges in the field, aiming to inform and guide future research efforts in this critical area of healthcare.  \nII. OVERVIEW OF THE PROBLEM SETUP  \nFigure 1. provides a diagram illustrating the problem setup. The status of a patient can be characterized by a variety of electronic healthcare data (EHR) data, including but not limited to 1) the radiological data, such as X-rays and CT scans; (2) symptoms of patients, such as fever, cough, shortness of breath, fatigue, muscle soreness, nausea, and diarrhea; (3) vital signs, such as body temperature, pulse rate, respiration rate, and blood pressure; (4) comorbidities, such as chronic obstructive pulmonary disease, dementia, heart disease, ","cbCaiuEQQVEV236H","https://ap.wps.com/l/cbCaiuEQQVEV236H","pdf",443982,1,7,"English","en",105,"# Introduction\n## Overview of the problem setup\n## Clinical knowledge-informed model design","[{\"question\":\"Why is interpretable machine learning important for COVID-19 prognosis?\",\"answer\":\"It supports transparent, understandable predictive models that help clinicians make informed decisions, especially for identifying high-risk individuals and allocating resources effectively.\"},{\"question\":\"What types of data are used in the reviewed prognosis problem setup?\",\"answer\":\"The review describes using multimodal EHR data such as radiology (X-rays/CT), symptoms, vital signs, comorbidities, and blood test measurements, along with temporal information from in-hospital events.\"},{\"question\":\"How do clinicians’ domain knowledge and machine learning patterns work together in these models?\",\"answer\":\"Human experts provide domain knowledge and practical experience, while machine learning derives data-driven progression patterns; these sources are fused to produce more informed decisions, with interpretability enhanced during model development.\"}]","Review of Interpretable Machine Learning Models for Disease Prognosis | PDF",1785677045,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"review-of-interpretable-machine-learning-models-for-disease-prognosis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/review-of-interpretable-machine-learning-models-for-disease-prognosis/117567/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is interpretable machine learning important for COVID-19 prognosis?","Question",{"text":75,"@type":76},"It supports transparent, understandable predictive models that help clinicians make informed decisions, especially for identifying high-risk individuals and allocating resources effectively.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What types of data are used in the reviewed prognosis problem setup?",{"text":80,"@type":76},"The review describes using multimodal EHR data such as radiology (X-rays/CT), symptoms, vital signs, comorbidities, and blood test measurements, along with temporal information from in-hospital events.",{"name":82,"@type":73,"acceptedAnswer":83},"How do clinicians’ domain knowledge and machine learning patterns work together in these models?",{"text":84,"@type":76},"Human experts provide domain knowledge and practical experience, while machine learning derives data-driven progression patterns; 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