[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-426240-105":59,"doc-detail-426240-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","extracting-cognitive-slums-scores-from-unstructured-national-veterans-clinical-notes-with-ai","EXTRACTING COGNITIVE SLUMS SCORES FROM UNSTRUCTURED NATIONAL VETERANS CLINICAL NOTES WITH AI","","Cognitive status assessment is crucial for estimating patient health risk and outcomes but is often absent from structured fields in the electronic health record. The Saint Louis University Mental Status (SLUMS) Examination is a widely used Veterans Affairs (VA) screening tool, and compared with MMSE it is more sensitive to earlier cognitive impairment. A natural language processing approach extracts SLUMS scores from unstructured EHR notes by locating “SLUMS” and a nearby number, using annotated notes and a precision-optimized rule-based regex system. Results include 83.0% accuracy, 99.6% precision, and scalability to millions of notes.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/extracting-cognitive-slums-scores-from-unstructured-national-veterans-clinical-notes-with-ai/426240/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/extracting-cognitive-slums-scores-from-unstructured-national-veterans-clinical-notes-with-ai/426240.png","ImageObject",300,407,{"name":92,"@type":93},"McQueen","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is extracting SLUMS scores from unstructured EHR notes important?","Question",{"text":112,"@type":113},"Cognitive status strongly relates to patient health risk, yet SLUMS information is rarely available in structured EHR data. Extracting it from free-text enables more complete cognitive assessment research.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed NLP method locate SLUMS scores in clinical notes?",{"text":117,"@type":113},"It searches for clinical notes containing the word “SLUMS” and a number within a 500-character window, then applies a rule-based regular-expression pattern matching system.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance did the algorithm achieve on annotated data?",{"text":121,"@type":113},"On annotated notes, it achieved 83.0% accuracy and 99.6% precision, with an F1 score of 78.2%, optimized for precision over recall.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},426240,1791099177,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":81},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Innovation in Aging, 2025, Vol. 9, No. S2 1123  \nAbstract citation ID: igaf122.3768  \nEXTRACTING COGNITIVE SLUMS SCORES FROM  \nUNSTRUCTURED NATIONAL VETERANS CLINICAL  \nNOTES WITH AI  \nRui Ouyang1, Christine Rizk2, Amir Sharafkhaneh3,  \nSanam Sharafkhaneh3, Jose Rios-Monterrosa3, Dashiell Helmer3, and Javad Razjouyan3, 1. Boston Veterans Healthcare Administration, Boston, Massachusetts, United States,  \n2. Michael E. DeBakey VA Medical Center, Houston, Texas, United States, 3. Baylor College of Medicine, Houston, Texas, United States  \nClinical assessment of cognitive status is critical to evaluating patient health risk and outcomes yet rarely found within the structured data in the electronic health record (EHR) . The Saint Louis University Mental Status (SLUMS) Examination is a cognitive screening tool widely used within the Veterans Affairs (VA).  \nCompared to the Mini-Mental State Examination (MMSE), the SLUMS score has greater sensitivity to earlier stages of cognitive impairment. We present a natural language processing (NLP) method for extracting SLUMS scores from unstructured EHR notes. We identified clinical notes from VA patients that contained the word “SLUMS”and a number within a 500 character window. Two researchers independently annotated 1,275 notes.  \nOf these, 899 contained a single SLUMS score and 376 contained missing, multiple, invalid (typo), or qualitative scores. We developed a rule-based system incorporating regular–expression–based pattern matching. The algorithm was developed on the full set of notes and optimized for precision over recall (i.e., only makes predictions when confident) . It achieved 83.0% accuracy and 99.6% precision with an F1 score of 78.2%. We further ran the algorithm on 2.96 million unannotated notes. The algorithm throughput exceeded 20 thousand notes per minute when run on a single laptop processor. This work demonstrates how NLP can extract SLUMS scores with high accuracy at scale from millions of unstructured clinical notes. By creating structured cognitive assessment data from information previously buried in free-form clinical text, our work enables cognitive impairment research at the population scale.","cbCailpb5qPvL7Vm","https://ap.wps.com/l/cbCailpb5qPvL7Vm","pdf",989405,"English","# Method Overview\n## Data Identification and Annotation\n## Rule-Based Extraction and Optimization\n# Performance and Scaling\n## Evaluation Metrics\n## Throughput and Population-Scale Enablement","[{\"question\":\"Why is extracting SLUMS scores from unstructured EHR notes important?\",\"answer\":\"Cognitive status strongly relates to patient health risk, yet SLUMS information is rarely available in structured EHR data. Extracting it from free-text enables more complete cognitive assessment research.\"},{\"question\":\"How does the proposed NLP method locate SLUMS scores in clinical notes?\",\"answer\":\"It searches for clinical notes containing the word “SLUMS” and a number within a 500-character window, then applies a rule-based regular-expression pattern matching system.\"},{\"question\":\"What performance did the algorithm achieve on annotated data?\",\"answer\":\"On annotated notes, it achieved 83.0% accuracy and 99.6% precision, with an F1 score of 78.2%, optimized for precision over recall.\"}]","EXTRACTING COGNITIVE SLUMS SCORES FROM UNSTRUCTURED NATIONAL VETERANS CLINICAL NOTES WITH AI | PDF",1790639881]