[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-447624-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-447624-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","predicting-chronic-kidney-disease-in-type-2-diabetes-using-natural-language-processing-on-healthcare-data","Predicting Chronic Kidney Disease in Type 2 Diabetes Using Natural Language Processing on Healthcare Data","","Type 2 diabetes mellitus patients receiving hospital care face substantial complications, including chronic kidney disease (CKD). This multicenter retrospective study evaluates unstructured free-text data from electronic health records, using natural language processing and machine learning, to build and validate a 2-year CKD prediction model for people with T2DM. Data from eight Spanish hospitals (2013–2018) produced a model chosen for balanced performance and clinical interpretability, then integrated into a web-based tool for early detection and risk stratification.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/predicting-chronic-kidney-disease-in-type-2-diabetes-using-natural-language-processing-on-healthcare-data/447624/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/predicting-chronic-kidney-disease-in-type-2-diabetes-using-natural-language-processing-on-healthcare-data/447624.png","ImageObject",300,407,{"name":42,"@type":43},"acoobca","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-05","2026-09-29",true,{"@type":52,"interactionType":53,"userInteractionCount":30},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What data sources and techniques were used to predict CKD in patients with type 2 diabetes?","Question",{"text":62,"@type":63},"The study used unstructured free-text data extracted from electronic health records. Natural language processing and machine learning techniques were applied, based on SNOMED CT terminology.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How was the predictive model developed and validated?",{"text":67,"@type":63},"Individuals with T2DM were identified from a cohort and separated into those with and without CKD at inclusion. For those without CKD, a 2-year predictive model was trained and validated using data from eight Spanish hospitals (2013–2018).",{"name":69,"@type":60,"acceptedAnswer":70},"Which model performed best and what was its basis for implementation?",{"text":71,"@type":63},"Logistic regression achieved the best overall performance, with an ROC area under the curve of 0.72 using 27 predictors. Reduced 10- and clinically refined 8-predictor models showed comparable performance, and the clinically refined model was selected for the web-based tool.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},447624,1790792341,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":30,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},3985747870733,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Kidney Diseases  \nResearch Article  \nKidney Dis 2026;12:18–28 DOI: 10. 1159/000547604  \nReceived: December 20, 2024  \nAccepted: June 25, 2025  \nPublished online: October 26, 2025  \nPredicting Chronic Kidney Disease in Type 2 Diabetes Using Natural Language Processing on Healthcare Data  \nJuan F. Navarro-González a, b, c, d Leopoldo Pérez de Isla e Gloria Cánovas Molinaf Miguel Ángel Brito-Sanfielg  \nDavid Emilio Barajas Galindoh Luís Ángel Cuellar Olmedoi Dídac Mauricioj Santiago Tofé Povedanok José Antonio Balsa Barrol  \nMatilde Rubio Almanzam, n José Juan Aparicio Sánchez o  \nMiren Sequera Mutiozabalo Belén Pimentel o Ana Pérez Domínguez o Carlos Arias-Cabralesp Víctor Fanjul p Antonio Jesús Blanco-Carrasco q Juan Francisco Merino Torres m, n  \naUnidad de Investigación y Servicio de Nefrología, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain; bRICORS2040, Instituto de Salud Carlos III, Madrid, Spain; cInstituto de Tecnologías Biomédicas, Universidad de La Laguna, Tenerife, Spain; d Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain; eServicio de Cardiología, Hospital Clínico San Carlos, Madrid, Spain; fServicio de Endocrinología y Nutrición, Hospital Universitario de Fuenlabrada, Madrid, Spain; gServicio de Endocrinología y Nutrición, Hospital Universitario Puerta de Hierro, Madrid, Spain; hServicio de Endocrinología y Nutrición, Hospital Universitario de León, León, Spain; iServicio de Endocrinología y Nutrición, Hospital Universitario Río Hortega, Valladolid, Spain; jServicio de Endocrinología y Nutrición, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; kServicio de Endocrinología y Nutrición, Hospital Universitari Son Espases, Mallorca, Spain; lServicio de Endocrinología y Nutrición, Hospital Universitario Infanta Sofía, Madrid, Spain; mServicio de Endocrinología y Nutrición, Departamento de Medicina, Hospital Universitari i Politècnic La Fe, Valencia, Spain; n Instituto Investigación Sanitaria La Fe, Universidad de Valencia, Valencia, Spain; oDepartamento Médico Cardiovascular, Renal y Metabolismo, AstraZeneca España, Madrid, Spain; pSAVANA Research Group, S.A., Madrid, Spain; qServiciode Endocrinología y Nutrición, Hospital Clínic de Barcelona, Barcelona, Spain  \nKeywords  \nChronic kidney disease · Machine learning · Natural language processing · Predictive model · Type 2 diabetes mellitus · Real-world data · Electronic health records  \nAntonio Jesús Blanco-Carrasco and Juan Francisco Merino Torres contributed equally to this work.  \n[karger@karger.com](karger@karger.com)[ ](karger@karger.com)[www.karger.com/kdd](www.karger.com/kdd)  \n© 2025 The Author(s) .  \nPublished by S. Karger AG, Basel  \nThis article is licensed under the Creative Commons Attribution 4.0 International License (CC BY) ([https://karger.com/Services/](https://karger.com/Services/)[ ](https://karger.com/Services/)[OpenAccessLicense](OpenAccessLicense)). Usage, derivative works and distribution are permitted provided that proper credit is given to the author and the original publisher.  \nCorrespondence to:  \nJuan F. Navarro-González, jnavgon[gobiernodecanarias.org](gobiernodecanarias.org)  \nAbstract  \nIntroduction: Persons with type 2 diabetes mellitus (T2DM) attending hospitals frequently experience major complications. We assessed the potential use of unstructured freetext data extracted from electronic health records (EHRs) using natural language processing (NLP) and machine learning (ML) to develop a predictive model for chronic kidney disease (CKD) in T2DM. Methods: This multicenter retrospective study included data from eight Spanish hospitals (2013–2018), extracted using NLP and ML techniques (EHRead®) based on SNOMED CT terminology. From a cohort of individuals with T2DM, we identified those with and without CKD at inclusion. Among individuals without CKD, we trained and validated a 2-year predictive model for CKD development. The model showing the","cbCaivpC6cszh8QL","https://ap.wps.com/l/cbCaivpC6cszh8QL","pdf",2624994,11,"English","# Abstract\n## Introduction\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What data sources and techniques were used to predict CKD in patients with type 2 diabetes?\",\"answer\":\"The study used unstructured free-text data extracted from electronic health records. Natural language processing and machine learning techniques were applied, based on SNOMED CT terminology.\"},{\"question\":\"How was the predictive model developed and validated?\",\"answer\":\"Individuals with T2DM were identified from a cohort and separated into those with and without CKD at inclusion. For those without CKD, a 2-year predictive model was trained and validated using data from eight Spanish hospitals (2013–2018).\"},{\"question\":\"Which model performed best and what was its basis for implementation?\",\"answer\":\"Logistic regression achieved the best overall performance, with an ROC area under the curve of 0.72 using 27 predictors. Reduced 10- and clinically refined 8-predictor models showed comparable performance, and the clinically refined model was selected for the web-based tool.\"}]","Predicting Chronic Kidney Disease in Type 2 Diabetes Using Natural Language Processing on Healthcare Data | PDF",1790721940,28]