[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122788-en":3,"doc-seo-122788-105":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},122788,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine Learning for Benchmarking Critical Care Outcomes","Machine learning for benchmarking critical care outcomes supports risk-adjusted performance evaluation by retrospectively comparing observed results with standards and predicted outcomes. A narrative review synthesizes findings from 2003–2023 literature on ML models for mortality, length of stay, and mechanical ventilation, summarizing progress in feature design, preprocessing, model selection, and validation. Reported applications address nonlinear relationships, class imbalance, missing data, and documentation variability, improving benchmark effectiveness while highlighting remaining gaps.","Review Article  \nHealthc Inform Res. 2023 October;29(4):301-314. [https://doi.org/10.4258/hir.2023.29.4.301](https://doi.org/10.4258/hir.2023.29.4.301)[ ](https://doi.org/10.4258/hir.2023.29.4.301)[pISSN 2093-3681](pISSN 2093-3681) • eISSN 2093-369X  \nMachine Learning for Benchmarking Critical Care Outcomes  \nLouis Atallah 1, Mohsen Nabian 1, Ludmila Brochini2, Pamela J. Amelung3  \n1Clinical Integration and Insights, Philips, Cambridge, MA, USA 2Clinical Integration and Insights, Philips, Eindhoven, The Netherlands 3EMR & Care Management, Philips, Cambridge, MA, USA  \nObjectives: Enhancing critical care efficacy involves evaluating and improving system functioning. Benchmarking, a retrospective comparison of results against standards, aids risk-adjusted assessment and helps healthcare providers identify areas for improvement based on observed and predicted outcomes. The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative review centers on key discoveries and outcomes to aid clinicians and researchers in selecting the optimal methodology for critical care benchmarking using ML. Methods: We used PubMed to search the literature from 2003 to 2023 regarding predictive models utilizing ML for mortality (592 articles), length of stay (143 articles), or mechanical ventilation (195 articles) . We supplemented the PubMed search with Google Scholar, making sure relevant articles were included. Given the narrative style, papers in the cohort were manually curated for a comprehensive reader perspective. Results: Our report presents comparative results for benchmarked outcomes and emphasizes advancements in feature types, preprocessing, model selection, and validation. It showcases instances where ML effectively tackled critical care outcome-prediction challenges, including nonlinear relationships, class imbalances, missing data, and documentation variability, leading to enhanced results. Conclusions: Although ML has provided novel tools to improve the benchmarking of critical care outcomes, areas that require further research include class imbalance, fairness, improved calibration, generalizability, and long-term validation of published models.  \nKeywords: Benchmarking, Critical Care, Length of Stay, Machine Learning, Mortality, Ventilation  \nSubmitted: December 9, 2022  \nRevised: August 23, 2023  \nAccepted: September 25, 2023  \nCorresponding Author  \nLouis Atallah  \nClinical Integration and Insights, Philips, 222 Jacobs Street, 7th Floor, Cambridge, MA 02141, USA. Tel: +1-617-798-8244, E-mail: [louis.atallah@philips.com](louis.atallah@philips.com) ([https://orcid.org/0000-0002-6657-319X](https://orcid.org/0000-0002-6657-319X))   \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([http://creativecommons.org/licenses/by](http://creativecommons.org/licenses/by)nc/4 .0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nⓒ 2023 The Korean Society of Medical Informatics  \nI. Introduction  \nPerformance comparison is an important aspect of benchmarking in critical care, whether to observe a critical care unit over time or to compare units, hospitals, or even health systems across geographic regions [1,2] . Benchmarking outcomes in critical care, such as mortality or length of stay, allows a risk-adjusted comparison with healthcare leaders as a proxy for quality and efficacy of care. Risk adjustment models have been the cornerstone for benchmarking outcomes in critical care. These models allow the prediction of outcomes to enable the benchmarking or comparison of actual versus  \nLouis Atallah et al  \npredicted outcomes among peers. Outcomes are difficul","cbCaieyfae2uIACh","https://ap.wps.com/l/cbCaieyfae2uIACh","pdf",296702,1,14,"English","en",105,"# Introduction\n## Benchmarking performance and risk-adjusted outcomes\n## Quality indicators and outcome measures\n## Mortality, length of stay, ventilation, and patient-reported outcomes\n# Methods\n## Literature search strategy (PubMed and Google Scholar)\n## Manual curation of narrative review papers\n# Results\n## Comparative benchmarking outcomes\n## Feature types, preprocessing, model selection, validation\n## Handling nonlinearities, class imbalance, missing data, documentation variability\n# Conclusions\n## Research needs: imbalance, fairness, calibration, generalizability, long-term validation","[{\"question\":\"What is the role of benchmarking in critical care outcomes?\",\"answer\":\"Benchmarking enables risk-adjusted comparison of actual outcomes against predicted standards. It helps identify improvement opportunities based on observed versus expected results.\"},{\"question\":\"Which critical care outcomes are commonly modeled using machine learning in the review?\",\"answer\":\"The review focuses on ML predictive models for mortality, length of stay, and mechanical ventilation outcomes.\"},{\"question\":\"What challenges does machine learning address when benchmarking critical care outcomes?\",\"answer\":\"ML methods are used to manage nonlinear relationships, class imbalance, missing data, and variability in clinical documentation, which can improve predictive benchmarking performance.\"}]","Machine Learning for Benchmarking Critical Care Outcomes | PDF",1785812899,35,{"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-for-benchmarking-critical-care-outcomes","",{"@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-for-benchmarking-critical-care-outcomes/122788/",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","2026-08-04",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 is the role of benchmarking in critical care outcomes?","Question",{"text":75,"@type":76},"Benchmarking enables risk-adjusted comparison of actual outcomes against predicted standards. It helps identify improvement opportunities based on observed versus expected results.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which critical care outcomes are commonly modeled using machine learning in the review?",{"text":80,"@type":76},"The review focuses on ML predictive models for mortality, length of stay, and mechanical ventilation outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"What challenges does machine learning address when benchmarking critical care outcomes?",{"text":84,"@type":76},"ML methods are used to manage nonlinear relationships, class imbalance, missing data, and variability in clinical documentation, which can improve predictive benchmarking performance.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]