[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117394-en":3,"doc-seo-117394-105":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},117394,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Using machine learning methods to predict all-cause somatic hospitalizations in adults - A systematic review","This systematic review examines how machine learning (ML) predicts all-cause somatic hospital admissions and readmissions among adults. Searches covered eight databases from inception to October 2023, extracting and assessing evidence using CHARMS for data extraction, PROBAST for bias and applicability, and TRIPOD for reporting quality. Of 7,543 screened studies, 116 met inclusion criteria. Models varied widely in data, algorithms, preprocessing, and validation; AUC was most used and boosting tree-based methods were frequently better. Only 5% were implemented clinically, and reporting quality adherence was poor, with common gaps in interpretation, code availability, external validation, calibration, and class-imbalance handling.","PLOS ONE  \nOPEN ACCESS  \nCitation: Askar M, Tafavvoghi M, Småbrekke L, Bongo LA, Svendsen K (2024) Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review. PLoS ONE 19(8): e0309175 . [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1371/journal.pone](10.1371/journal.pone).0309175  \nEditor: Tariq Jamal Siddiqi, The University of Mississippi Medical Center, UNITED STATES OF AMERICA  \nReceived: February 1, 2024  \nAccepted: August 6, 2024  \nPublished: August 23, 2024  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0309175](https://doi.org/10.1371/journal.pone.0309175)  \n[Copyright:](Copyright:) © [2024 Askar et al](2024 Askar et al). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: This is a systematic review. The extracted data is available in the supplementary material.  \nRESEARCH ARTICLE  \nUsing machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review  \nMohsen Askar1 *, Masoud Tafavvoghi2, Lars Småbrekke1, Lars Ailo Bongo2, Kristian Svendsen1  \n1 Faculty of Health Sciences, Department of Pharmacy, UiT-The Arctic University of Norway, Tromsø , Norway, 2 Faculty of Science and Technology, Department of Computer Science, UiT-The Arctic University of Norway, Tromsø , Norway  \n* [mohsen.g.askar@uit.no](mohsen.g.askar@uit.no)  \nAbstract  \nAim  \nIn this review, we investigated how Machine Learning (ML) was utilized to predict all-cause somatic hospital admissions and readmissions in adults.  \nMethods  \nWe searched eight databases (PubMed, Embase, Web of Science, CINAHL, ProQuest, OpenGrey, WorldCat, and MedNar) from their inception date to October 2023, and included records that predicted all-cause somatic hospital admissions and readmissions of adults using ML methodology. We used the CHARMS checklist for data extraction, PROBAST for bias and applicability assessment, and TRIPOD for reporting quality.  \nResults  \nWe screened 7,543 studies of which 163 full-text records were read and 116 met the review inclusion criteria. Among these, 45 predicted admission, 70 predicted readmission, and one study predicted both. There was a substantial variety in the types of datasets, algorithms, features, data preprocessing steps, evaluation, and validation methods. The most used types of features were demographics, diagnoses, vital signs, and laboratory tests. Area Under the ROC curve (AUC) was the most used evaluation metric. Models trained using boosting tree-based algorithms often performed better compared to others. ML algorithms commonly outperformed traditional regression techniques. Sixteen studies used Natural language processing (NLP) of clinical notes for prediction, all studies yielded good results. The overall adherence to reporting quality was poor in the review studies. Only five percent of models were implemented in clinical practice. The most frequently inadequately addressed methodological aspects were: providing model interpretations on the individual patient level, full code availability, performing external validation, calibrating models, and handling class imbalance.  \nFunding: The publication charges for this article have been funded by a grant from the publication fund of UiT The Arctic University of Norway. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nCompeting interests: The authors have declared that no competing interests exist.  \nConclusion  \nThi","cbCaivFq5a4CmXtu","https://ap.wps.com/l/cbCaivFq5a4CmXtu","pdf",1676609,1,21,"English","en",105,"# Abstract\n## Aim\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Background and rationale","[{\"question\":\"What was the main aim of this review?\",\"answer\":\"To investigate how machine learning was used to predict all-cause somatic hospital admissions and readmissions in adults.\"},{\"question\":\"Which databases and review standards were used?\",\"answer\":\"Eight databases were searched from inception to October 2023, and CHARMS, PROBAST, and TRIPOD were used for extraction, bias/applicability assessment, and reporting quality.\"},{\"question\":\"What were the key findings about model performance and reporting?\",\"answer\":\"Evaluation metrics varied, AUC was most common, boosting tree-based algorithms often performed better, and ML models frequently outperformed traditional regression. Reporting quality was generally poor, and only 5% of models were implemented in clinical practice.\"}]","Using machine learning methods to predict all-cause somatic hospitalizations in adults - A systematic review | PDF",1785675638,53,{"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},"using-machine-learning-methods-to-predict-all-cause-somatic-hospitalizations-in-adults-a-systematic-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/using-machine-learning-methods-to-predict-all-cause-somatic-hospitalizations-in-adults-a-systematic-review/117394/",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},"What was the main aim of this review?","Question",{"text":75,"@type":76},"To investigate how machine learning was used to predict all-cause somatic hospital admissions and readmissions in adults.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which databases and review standards were used?",{"text":80,"@type":76},"Eight databases were searched from inception to October 2023, and CHARMS, PROBAST, and TRIPOD were used for extraction, bias/applicability assessment, and reporting quality.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the key findings about model performance and reporting?",{"text":84,"@type":76},"Evaluation metrics varied, AUC was most common, boosting tree-based algorithms often performed better, and ML models frequently outperformed traditional regression. 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