[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128521-en":3,"doc-seo-128521-105":30,"detail-sidebar-cat-0-en-105":96},{"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},128521,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","COVID‑Net Biochem - an explainability‑driven framework to building machine learning models for predicting survival and kidney injury of COVID‑19 patients from clinical and biochemistry data","COVID‑Net Biochem presents a two‑stage, explainability‑driven framework for constructing transparent machine learning models to predict COVID‑19 patient survival and the risk of acute kidney injury during hospitalization. The approach integrates clinician‑guided dataset preprocessing with quantitative explainability analyses to surface key biomarkers, assess decision validity, and support bias identification. Models are trained on a curated clinical and biochemistry benchmark from a 1366‑patient cohort, achieving strong predictive performance and enabling interpretable, trustworthy clinical decision support.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nCOVID‑Net Biochem: an explainability‑driven framework to building machine learning models for predicting survival and kidney injury ofCOVID‑19 patients from clinical and biochemistry data  \nHossein Aboutalebi1,3*, Maya Pavlova2, Mohammad Javad Shafiee2,3,4, Adrian Florea 5, Andrew Hryniowski2,4 & Alexander Wong1,2,3,4  \nSince the World Health Organization declared COVID‑19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS‑CoV‑2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has not only been the accurate detection of positive cases but also the efficient prediction of risks associated with complications and patient survival probabilities. These tasks entail considerable clinical resource allocation and attention. In this study, we introduce COVID‑Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID‑19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization, utilizing clinical and biochemical data in a transparent, systematic approach. The proposed approach advances machine learning model design by seamlessly integrating domain expertise with explainability tools, enabling model decisions to be based on key biomarkers. This fosters a more transparent and interpretable decision‑making process made by machines specifically for medical applications. More specifically, the framework comprises two phases: In the first phase, referred to as the “clinician‑guided design” phase, the dataset is preprocessed using explainable AI and domain expert input. To better demonstrate this phase, we prepared a benchmark dataset of carefully curated clinical and biochemical markers based on clinician assessments for survival and kidney injury prediction in COVID‑19 patients. This dataset was selected from a patient cohort of 1366 individuals at Stony Brook University. Moreover, we designed and trained a diverse collection of machine learning models, encompassing gradient‑based boosting tree architectures and deep transformer architectures, specifically for survival and kidney injury prediction based on the selected markers. In the second phase, called the “explainability‑driven design refinement” phase, the proposed framework employs explainability methods to not only gain a deeper understanding of each model’s decision‑making process but also to identify the overall impact of individual clinical and biochemical markers for bias identification. In this context, we used the models constructed in the previous phase for the prediction task and analyzed the explainability outcomes alongside a clinician with over 8 years of experience to gain a deeper understanding of the clinical validity of the decisions made. The explainability‑driven insights obtained, in conjunction with the associated clinical  \n1Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada. 2Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada. 3Waterloo Artificial Intelligence Institute, University of Waterloo, Waterloo, Canada. 4DarwinAI Corp., Waterloo, Canada. 5Department of Emergency Medicine, McGill University, Montreal, Canada.* email: [haboutal@uwaterloo.ca](haboutal@uwaterloo.ca)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nfeedback, are then utilized to guide and refine the training policies and architectural design iteratively. This process aims to enhance not only the prediction performance but also the clinical validity and trustworthiness of the final machine learning models. Employing the proposed explainability‑driven framework, we attained 93.55% accuracy in survival prediction and 88.05% accuracy in predicting kidney injury complications. The models have be","cbCaiooWYsRGAxPY","https://ap.wps.com/l/cbCaiooWYsRGAxPY","pdf",3683415,1,14,"English","en",105,"# Introduction\n## Clinical prediction challenges in COVID‑19\n## COVID‑Net Biochem overview\n# Framework Design\n## Clinician‑guided design phase\n## Explainability‑driven design refinement phase\n# Data and Models\n## Benchmark dataset and cohort\n## Machine learning model training\n# Explainability and Clinical Validation\n## Biomarker impact and bias identification\n## Iterative refinement of training and architecture\n# Results and Availability\n## Predictive performance metrics\n## Open‑source model availability\n# Conclusion","[{\"question\":\"What predictions does COVID‑Net Biochem support for hospitalized COVID‑19 patients?\",\"answer\":\"It predicts patient survival probability and the likelihood of developing acute kidney injury (AKI) during hospitalization.\"},{\"question\":\"How does the framework incorporate clinician input and explainability?\",\"answer\":\"In the first phase, clinicians guide dataset preprocessing using explainable AI and expert input. In the second phase, quantitative explainability methods refine training policies and architecture while identifying biomarker impact and potential bias.\"},{\"question\":\"What data and patient cohort are used to build the benchmark dataset?\",\"answer\":\"The benchmark uses carefully curated clinical and biochemical markers selected from a patient cohort of 1366 individuals at Stony Brook University.\"},{\"question\":\"What performance was reported for survival and AKI prediction?\",\"answer\":\"The study reports 93.55% accuracy for survival prediction and 88.05% accuracy for predicting kidney injury complications, and the models are released via an open‑source platform.\"}]","COVID‑Net Biochem - an explainability‑driven framework to building machine learning models for predicting survival and kidney injury of COVID‑19 patients from clinical and biochemistry data | PDF",1786001512,35,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":28},"covidnet-biochem-an-explainabilitydriven-framework-to-building-machine-learning-models-for-predicting-survival-and-kidney-injury-of-covid19-patients-from-clinical-and-biochemistry-data","",{"@graph":36,"@context":90},[37,54,69],{"@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/covidnet-biochem-an-explainabilitydriven-framework-to-building-machine-learning-models-for-predicting-survival-and-kidney-injury-of-covid19-patients-from-clinical-and-biochemistry-data/128521/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What predictions does COVID‑Net Biochem support for hospitalized COVID‑19 patients?","Question",{"text":76,"@type":77},"It predicts patient survival probability and the likelihood of developing acute kidney injury (AKI) during hospitalization.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the framework incorporate clinician input and explainability?",{"text":81,"@type":77},"In the first phase, clinicians guide dataset preprocessing using explainable AI and expert input. In the second phase, quantitative explainability methods refine training policies and architecture while identifying biomarker impact and potential bias.",{"name":83,"@type":74,"acceptedAnswer":84},"What data and patient cohort are used to build the benchmark dataset?",{"text":85,"@type":77},"The benchmark uses carefully curated clinical and biochemical markers selected from a patient cohort of 1366 individuals at Stony Brook University.",{"name":87,"@type":74,"acceptedAnswer":88},"What performance was reported for survival and AKI prediction?",{"text":89,"@type":77},"The study reports 93.55% accuracy for survival prediction and 88.05% accuracy for predicting kidney injury complications, and the models are released via an open‑source platform.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},5,"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":46,"category_name":142,"show_sort_weight":111,"slug":143},19,"General","general"]