[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122011-en":3,"doc-seo-122011-105":29,"detail-sidebar-cat-0-en-105":81},{"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":11},122011,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Comments on Contemporary Uses of Machine Learning for Electronic Health Records","Electronic health record (EHR) management and clinical decision workflows increasingly rely on machine learning (ML) to automate how data are organized, displayed, and used diagnostically. The commentary reviews current ML applications to structured EHR inputs such as demographics, tests, and biometric or genomic measures, and also discusses near-term directions including sequential decisionmaking via reinforcement learning and clinician-focused interface optimization. Benefits are weighed alongside limitations such as bias, imprecision, and added physician workload, alongside emerging emphasis on unstructured data and large language models.","INVITED COMMENTARY  \nComments on Contemporary Uses of Machine Learning for Electronic Health Records  \nJordan Bryan, Didong Li  \nVarious decisions concerning the management, display, and diagnostic use of electronic health records (EHR) data can be automated using machine learning (ML). We describe how ML is currently applied to EHR data and how it may be applied in the near future. Both benefits and shortcomings of ML are considered.  \nIntroduction  \nThe use of machine learning (ML) for the analysis of  \nelectronic health record (EHR) data has become more frequent over the course of the last two decades. Today, two complementary forces all but ensure that this trend will continue into the near future: EHRs are growing in size and complexity as more patients interact with health care systems and as a broader array of screening technologies are deployed in clinical practice, while at the same time public and private investment is spurring the development of new ML methods that are capable of handling ever more diverse data types. As ML becomes more integrated into decisionmaking using EHR data, it is important that practitioners and policymakers understand how ML is being applied to EHRs, and how the use of ML may both improve and complicate health care.  \nML—sometimes used synonymously with artificial intelligence (AI)—is an evocative term, which may mean different things to different audiences. For the purposes of this commentary, we define ML as a class of methods for deriving decision rules by using a combination of data, mathematical or statistical principles, and computer software. Health care decisions based on ML have the potential to be faster, more precise, less expensive, and less biased than those attainable purely through clinical judgement or case review. Hence, in the last two decades there has been intense interest in applying ML to the analysis of EHR data, where sample sizes tend to be large and where decision rules may inform public health assessments, clinical trial design, optimal treatment regimes, or clinical decision support (CDS) .  \nIn many respects, current uses of ML for EHR data represent innovations on themes that emerged in the 1990s as EHRs became more widely adopted by hospital systems. Even at that time, automating aspects of record-keeping  \nand CDS were seen as key potential benefits of moving from paper records to EHRs [1] . Now, various ML classification and regression models—which take in the demographic information, test results, and biometric or genomic measurements in structured EHR data—are supplementing or replacing clinical decision rules based on commonknowledge health care guidelines. These models may be integrated directly into EHR systems to identify patient subpopulations of interest and to assign diagnostic labels to patients with any number of rare or common disorders in real time [2–7] . The paradigm of reinforcement learning (RL), which constitutes a subset of ML, is also being used to inform health care decisions that must be made sequentially in response to a course of patient outcomes. For instance, RL has been applied to design treatment regimens for patients in intensive care [8] and for those enrolled in clinical trials [9] . In addition to patient-centric uses, physician-centric uses of ML have recently been studied and piloted in clinics. Most commonly, in order to reduce the stress associated with interacting with EHRs, experimental software platforms have been developed to use ML to prioritize only the most relevant information for display on clinicians’ EHR interface [10, 11] . ML is also increasingly being used to shape decisions for hospital resource management, such as scheduling hospital admissions from the emergency department (ED) or anticipating 30-day readmission to the hospital [12, 13] .  \nEmerging Trends  \nWhile much of the activity in ML research for EHR data is a continuation of what came before, there are at least two factors that distinguish the cur","cbCaimUBnNRH25wP","https://ap.wps.com/l/cbCaimUBnNRH25wP","pdf",79657,1,3,"English","en",105,"# Introduction\n## Emerging Trends","[{\"question\":\"What shortcomings and risks of ML in EHR settings are discussed?\",\"answer\":\"Reported studies point to sources of bias, imprecision, and increased time-load for physicians when ML recommendation systems are deployed. Future research is expected to address practical challenges that arise as ML is integrated into real-time clinical decision support.\"}]","Comments on Contemporary Uses of Machine Learning for Electronic Health Records | PDF",1785808278,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":76,"head_meta":78,"extra_data":80,"updated_unix":28},"comments-on-contemporary-uses-of-machine-learning-for-electronic-health-records","",{"@graph":35,"@context":75},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/comments-on-contemporary-uses-of-machine-learning-for-electronic-health-records/122011/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69],{"name":70,"@type":71,"acceptedAnswer":72},"What shortcomings and risks of ML in EHR settings are discussed?","Question",{"text":73,"@type":74},"Reported studies point to sources of bias, imprecision, and increased time-load for physicians when ML recommendation systems are deployed. Future research is expected to address practical challenges that arise as ML is integrated into real-time clinical decision support.","Answer","https://schema.org",{"og:url":50,"og:type":77,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":79,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":82},[83,87,91,95,100,105,110,113,118,121,125],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":111,"slug":112},30,"research-report",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},9,"Religion & Spirituality",20,"religion-spirituality",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":116,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":96,"slug":128},19,"General","general"]