[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125206-en":3,"doc-seo-125206-105":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":20,"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},125206,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning Applications in Medical Prognostics - A Comprehensive Review","Machine learning (ML) advances medical prognostics by combining sophisticated algorithms with clinical data to improve disease prediction, risk assessment, and patient outcome forecasting. This comprehensive review evaluates major ML approaches and compares their performance, strengths, and weaknesses across representative tasks. It also addresses interpretability requirements and practical constraints such as data quality and privacy. Future directions include integrating multi-modal data, applying transfer learning, and enabling continuous learning to strengthen clinical usability and predictive impact.","arXiv :2408 .02344v1 [ cs .LG] 5 Aug 2024  \nTitle: Machine Learning Applications in Medical Prognostics: A Comprehensive Review  \nDr Michael Fascia. Edinburg Napier University [Email:m.fascia2@napier.ac.uk](Email:m.fascia2@napier.ac.uk)  \n1May 2024  \nAbstract  \nMachine learning (ML) has revolutionized medical prognostics by integrating advanced algorithms with clinical data to enhance disease prediction, risk assessment, and patient outcome forecasting. This comprehensive review critically examines the application of various ML techniques in medical prognostics, focusing on their e􀀞cacy, challenges, and future directions. The methodologies discussed include Random Forest (RF) for sepsis prediction, logistic regression for cardiovascular risk assessment, Convolutional Neural Networks (CNNs) for cancer detection, and Long Short-Term Memory (LSTM) networks for predicting clinical deterioration. RF models demonstrate robust performance in handling high-dimensional data and capturing non-linear relationships, making them particularly e􀀛ective for sepsis prediction. Logistic regression remains valuable for its interpretability and ease of use in cardiovascular risk assessment. CNNshave shown exceptional accuracy in cancer detection, leveraging their ability to learn complex visual patterns from medical imaging. LSTM networks excel in analyzing temporal data, providing accurate predictions of clinical deterioration. The review highlights the strengths and limitations of each technique, the importance of model interpretability, and the challenges of data quality and privacy. Future research directions include the integration of multi-modal data sources, the application of transfer learning, and the development of continuous learning systems. These advancements aim to enhance the predictive power and clinical applicability of ML models, ultimately improving patient outcomes in  \nhealthcare settings.  \nIntroduction  \nMachine learning (ML) has emerged as a transformative force in healthcare, particularly in the realm of medical prognostics. The integration of ML techniques with clinical data has revolutionized disease prediction, risk assessment, and patient outcome forecasting (Rajkomar et al., 2019) . This review aims to critically examine the application of various ML algorithms in medical prognostics, evaluate their e􀀞cacy, and explore the challenges and future directions in this rapidly evolving 􀀜eld. The objectives of this review are threefold: (1) to provide a comprehensive overview of key ML techniques employed in medical prognostics, (2) to analyze their performance across di􀀛erent medical applications, and (3) to identify current limitations and emerging trends in the 􀀜eld.  \nMethodology  \nThis review employed a systematic approach to identify and analyze relevant studies on ML applications in medical prognostics. The search strategy utilized multiple databases, including PubMed, IEEE Xplore, and Google Scholar, with keywords such as \"machine learning,\" \"medical prognostics,\" \"healthcare prediction,\" and speci􀀜c algorithm names. Studies were selected based on their relevance, methodological rigor, and impact in the 􀀜eld, with a focus on publications from the last 􀀜ve years to ensure currency of information. The methodology was designed to ensure a comprehensive and unbiased review of the current state of ML applications in medical prognostics. The search process was conducted in three phases: identi􀀜cation, screening, and eligibility assessment. In the identi-􀀜cation phase, Boolean operators were used to combine search terms, such as (\"machine learning\" OR \"arti􀀜cial intelligence\") AND (\"medical prognostics\" OR\"disease prediction\") AND (\"healthcare\" OR \"clinical decision support\") . This initial search yielded a total of 1,247 potentially relevant articles. The screening phase involved reviewing titles and abstracts to exclude studies that did not meet the prede􀀜ned inclusion criteria. These criteria included: (1) primary resea","cbCailbIbsGlqttK","https://ap.wps.com/l/cbCailbIbsGlqttK","pdf",227977,1,30,"English","en",105,"# Introduction\n# Methodology","[{\"question\":\"What is the purpose of the review on medical prognostics?\",\"answer\":\"The review critically examines ML algorithms used in medical prognostics, evaluates their effectiveness across applications, and identifies limitations and emerging trends.\"},{\"question\":\"How were relevant studies selected for the review?\",\"answer\":\"The review used a systematic multi-database search (PubMed, IEEE Xplore, Google Scholar), followed by screening of titles/abstracts and eligibility assessment using full-text review by multiple researchers.\"},{\"question\":\"Which assessment and reporting standards were used to ensure rigor?\",\"answer\":\"A quality assessment tool adapted from the CASP checklist was applied to categorize study quality, and PRISMA guidelines were used to improve transparency and reproducibility.\"}]","Machine Learning Applications in Medical Prognostics - A Comprehensive Review | PDF",1785897394,76,{"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},"machine-learning-applications-in-medical-prognostics-a-comprehensive-review","",{"@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/machine-learning-applications-in-medical-prognostics-a-comprehensive-review/125206/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the purpose of the review on medical prognostics?","Question",{"text":75,"@type":76},"The review critically examines ML algorithms used in medical prognostics, evaluates their effectiveness across applications, and identifies limitations and emerging trends.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were relevant studies selected for the review?",{"text":80,"@type":76},"The review used a systematic multi-database search (PubMed, IEEE Xplore, Google Scholar), followed by screening of titles/abstracts and eligibility assessment using full-text review by multiple researchers.",{"name":82,"@type":73,"acceptedAnswer":83},"Which assessment and reporting standards were used to ensure rigor?",{"text":84,"@type":76},"A quality assessment tool adapted from the CASP checklist was applied to categorize study quality, and PRISMA guidelines were used to improve transparency and reproducibility.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]