[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128085-en":3,"doc-seo-128085-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128085,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data - prospective observational study","Warfarin dosing varies substantially across individuals, making accurate prediction of blood levels essential for personalized anticoagulation management. This prospective observational study uses clinical longitudinal data from 246 cardiac surgery patients to build an information fusion perturbation theory–based machine-learning framework. Continuous and categorical variables are transformed via moving-average distance and Euclidean distance, then evaluated with regression and classification approaches. Random forest best predicts continuous INR targets, while GDA best predicts categorical INR-range probability.","’Experimental Research  \nMachine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data from cardiac surgery patients: a prospective observational study  \nLing Xue, MSca,g , Shan He, MSch,i , Rajeev K. Singla, PhDf,n , Qiong Qin, MSca , Yinglong Ding, MScb,d , Linsheng Liu, PhDa , Xiaoliang Ding, PhDa , Harbil Bediaga-Bañeres, MSci,j , Sonia Arrasate, PhDh , Aliuska Durado-Sanchez, PhDi,k , Yuzhen Zhang, PhDc , Zhenya Shen, PhDb,d,*, Bairong Shen, PhDf,*, Liyan Miao, PhDa,e,*, Humberto González-Díaz, PhDh,l,m  \nBackground: Warfarin is a common oral anticoagulant, and its effects vary widely among individuals. Numerous dose-prediction algorithms have been reported based on cross-sectional data generated via multiple linear regression or machine learning. This study aimed to construct an information fusion perturbation theory and machine-learning prediction model of warfarin blood levels based on clinical longitudinal data from cardiac surgery patients.  \nMethods and material: The data of 246 patients were obtained from electronic medical records. Continuous variables were processed by calculating the distance of the raw data with the moving average (MA Δvki(sj)), and categorical variables in different attribute groups were processed using Euclidean distance (ED ǁΔvk(sj)ǁ) . Regression and classification analyses were performed on the raw data, MA Δvki(sj), and ED ǁΔvk(sj)ǁ . Different machine-learning algorithms were chosen for the STATISTICA and WEKA software. Results: The random forest (RF) algorithm was the best for predicting continuous outputs using the raw data. The correlation coefficients ofthe RF algorithm were 0.978 and 0.595 for the training and validation sets, respectively, and the mean absolute errors were 0.135 and 0.362 for the training and validation sets, respectively. The proportion of ideal predictions of the RF algorithm was 59.0% . General discriminant analysis (GDA) was the best algorithm for predicting the categorical outputs using the MA Δvki(sj) data. The GDA algorithm’s total true positive rate (TPR) was 95.4% and 95.6% for the training and validation sets, respectively, with MA Δvki(sj) data. Conclusions: An information fusion perturbation theory and machine-learning model for predicting warfarin blood levels was established. A model based on the RF algorithm could be used to predict the target international normalized ratio (INR), and a model based on the GDA algorithm could be used to predict the probability of being within the target INR range under different clinical scenarios.  \nKeywords: cardiac surgery, information fusion, machine learning, personalized medicine, perturbation theory, warfarin  \naDepartment of Pharmacy, the First Affiliated Hospital of Soochow University, bDepartment of Cardiovascular Surgery, the First Affiliated Hospital of Soochow University, cDepartment of Cardiology, the First Affiliated Hospital of Soochow University, dInstitute for Cardiovascular Science, Soochow University, eInstitute for Interdisciplinary Drug Research and Translational Sciences, Soochow University, fJoint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China, gDepartment of Pharmacology, Faculty of Medicine, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, hDepartment of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain, iIKERDATA S.L., ZITEK, University of The Basque Country (UPV/ EHU), Bilbao, Basque Country, jDepartment of Painting, Faculty of Fine Arts, University of the Basque Country UPV/EHU, 48940, Leioa, Biscay, kDepartment of Public Law, Faculty of Law, University of The Basque Country (UPV/EHU), Leioa, Biscay, Basque, Country, l","cbCaimFO0NTgN65b","https://ap.wps.com/l/cbCaimFO0NTgN65b","pdf",986730,2,1,13,"English","en",105,"# Background\n# Methods and material\n## Data and variable processing\n## Modeling and evaluation\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"Why is warfarin blood-level prediction important in clinical practice?\",\"answer\":\"Warfarin is a common oral anticoagulant whose effects vary widely between individuals, so reliable prediction supports safer, more individualized dosing. The study targets improved prediction of INR-related outcomes using patient-specific longitudinal data.\"},{\"question\":\"How does the study process continuous and categorical variables?\",\"answer\":\"Continuous variables are processed by computing distance from raw data using a moving average (MA Δvki(sj)). Categorical variables across attribute groups are processed using Euclidean distance (ED ǁΔvk(sj)ǁ). Regression and classification analyses are then applied to raw data and these processed representations.\"},{\"question\":\"Which machine-learning models performed best and for what outputs?\",\"answer\":\"Random forest (RF) performed best for continuous outputs using raw data, achieving high correlation on training and validation sets and lower mean absolute error. For categorical outputs using MA Δvki(sj) data, general discriminant analysis (GDA) produced the best discrimination performance with high true positive rates.\"}]","Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data - prospective observational study | PDF",1785944724,33,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-guided-prediction-of-warfarin-blood-levels-for-personalized-medicine-based-on-clinical-longitudinal-data-prospective-observational-study","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-guided-prediction-of-warfarin-blood-levels-for-personalized-medicine-based-on-clinical-longitudinal-data-prospective-observational-study/128085/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is warfarin blood-level prediction important in clinical practice?","Question",{"text":76,"@type":77},"Warfarin is a common oral anticoagulant whose effects vary widely between individuals, so reliable prediction supports safer, more individualized dosing. The study targets improved prediction of INR-related outcomes using patient-specific longitudinal data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study process continuous and categorical variables?",{"text":81,"@type":77},"Continuous variables are processed by computing distance from raw data using a moving average (MA Δvki(sj)). Categorical variables across attribute groups are processed using Euclidean distance (ED ǁΔvk(sj)ǁ). Regression and classification analyses are then applied to raw data and these processed representations.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning models performed best and for what outputs?",{"text":85,"@type":77},"Random forest (RF) performed best for continuous outputs using raw data, achieving high correlation on training and validation sets and lower mean absolute error. For categorical outputs using MA Δvki(sj) data, general discriminant analysis (GDA) produced the best discrimination performance with high true positive rates.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]