[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124899-en":3,"doc-seo-124899-105":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},124899,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","A machine learning approach to identify groups of patients with hematological malignant disorders","Vaccination against SARS-CoV-2 in immunocompromised patients with hematologic malignancies is essential, yet many patients fail to develop a serological response, leaving them vulnerable to severe breakthrough infections. This study applies machine learning to model post-vaccination serological response using patient-specific features learned from a cohort of 1166 individuals. An explainable pipeline based on SHAP and PCA clusters patients into four interpretable groups, supporting clinician- and biologist-relevant risk stratification and guidance for healthcare decision-making.","Computer Methods and Programs in Biomedicine 246 (2024) 108011  \nContents lists available at ScienceDirect  \nComputer Methods and Programs in Biomedicine  \njournal [homepage:](homepage: www.elsevier.com/locate/cmpb)[ www.elsevier.com/locate/cmpb](homepage: www.elsevier.com/locate/cmpb)  \n| A machine learning approach to identify groups of patients with hematological malignant disorders |  |  |  |\n| --- | --- | --- | --- |\n| Pablo Rodríguez-Belenguera, Jos´e Luis Pi˜nana b, c, Manuel S´anchez-Monta˜´nes d, *, Emilio SoriaOlivase, Marcelino Martínez-Sober e, Antonio J. Serrano-L´opez e\u003Cbr>a Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain\u003Cbr>b Hematology Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain\u003Cbr>c Fundaci´on INCLIVA, Instituto de Investigaci´on Sanitaria Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain d Department of Computer Science, Escuela Polit´ecnica Superior, Universidad Aut´onoma de Madrid, 28049 Madrid, Spain e IDAL, Intelligent Data Analysis Laboratory, ETSE, Universitat de Val`encia, 46100 Valencia, Spain |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Machine learning\u003Cbr>High risk groups identification Hematological disease COVID-19\u003Cbr>Serological response SARS-CoV-2 mRNA vaccines Explainable AI (XAI) |  | Background and Objective: Vaccination against SARS-CoV-2 in immunocompromised patients with hematologic malignancies (HM) is crucial to reduce the severity of COVID-19. Despite vaccination efforts, over a third of HM patients remain unresponsive, increasing their risk of severe breakthrough infections. This study aims to leverage machine learning’s adaptability to COVID-19 dynamics, efficiently selecting patient-specific features to enhance predictions and improve healthcare strategies. Highlighting the complex COVID-hematology connection, the focus is on interpretable machine learning to provide valuable insights to clinicians and biologists.\u003Cbr>Methods: The study evaluated a dataset with 1166 patients with hematological diseases. The output was the achievement or non-achievement of a serological response after full COVID-19 vaccination. Various machine learning methods were applied, with the best model selected based on metrics such as the Area Under the Curve (AUC), Sensitivity, Specificity, and Matthew Correlation Coefficient (MCC). Individual SHAP values were obtained for the best model, and Principal Component Analysis (PCA) was applied to these values. The patient profiles were then analyzed within identified clusters.\u003Cbr>Results: Support vector machine (SVM) emerged as the best-performing model. PCA applied to SVM-derived SHAP values resulted in four perfectly separated clusters. These clusters are characterized by the proportion of patients that generate antibodies (PPGA). Cluster 1, with the second-highest PPGA (69.91%), included patients with aggressive diseases and factors contributing to increased immunodeficiency. Cluster 2 had the lowest PPGA (33.3%), but the small sample size limited conclusive findings. Cluster 3, representing the majority of the population, exhibited a high rate of antibody generation (84.39%) and a better prognosis compared to cluster 1. Cluster 4, with a PPGA of 66.33%, included patients with B-cell non-Hodgkin’s lymphoma on corticosteroid therapy.\u003Cbr>Conclusions: The methodology successfully identified four separate patient clusters using Machine Learning and Explainable AI (XAI). We then analyzed each cluster based on the percentage of HM patients who generated antibodies after COVID-19 vaccination. The study suggests the methodology’s potential applicability to other diseases, highlighting the importance of interpretable ML in healthcare research and decision-making. |  |\n\n1. Introduction  \nOver time, groups at higher risk of suffering greater consequences","cbCaiuvdDt6GoAl4","https://ap.wps.com/l/cbCaiuvdDt6GoAl4","pdf",2005809,1,9,"English","en",105,"# Introduction\n# Methods\n## Dataset and outcomes\n## Model selection and evaluation\n## Explainable AI with SHAP and PCA\n## Patient clustering\n# Results\n## Best-performing model\n## Cluster characterization by PPGA\n# Conclusions","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study addresses the difficulty of predicting which hematologic malignancy patients will mount a serological response after full COVID-19 vaccination, given that many remain unresponsive and face severe breakthrough infections.\"},{\"question\":\"How were patient groups identified?\",\"answer\":\"The approach trains machine learning models to predict serological response, then uses SHAP values from the best model followed by PCA to separate patients into four clusters.\"},{\"question\":\"Which machine learning model performed best and what characterized the clusters?\",\"answer\":\"A support vector machine (SVM) was the best-performing model. Clusters were characterized by the proportion of patients generating antibodies (PPGA), with different underlying disease contexts described for each group.\"}]","A machine learning approach to identify groups of patients with hematological malignant disorders | PDF",1785895292,23,{"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},"a-machine-learning-approach-to-identify-groups-of-patients-with-hematological-malignant-disorders","",{"@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/a-machine-learning-approach-to-identify-groups-of-patients-with-hematological-malignant-disorders/124899/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study address?","Question",{"text":75,"@type":76},"The study addresses the difficulty of predicting which hematologic malignancy patients will mount a serological response after full COVID-19 vaccination, given that many remain unresponsive and face severe breakthrough infections.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were patient groups identified?",{"text":80,"@type":76},"The approach trains machine learning models to predict serological response, then uses SHAP values from the best model followed by PCA to separate patients into four clusters.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what characterized the clusters?",{"text":84,"@type":76},"A support vector machine (SVM) was the best-performing model. Clusters were characterized by the proportion of patients generating antibodies (PPGA), with different underlying disease contexts described for each group.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]