[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117240-en":3,"doc-seo-117240-105":30,"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":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},117240,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings - Research Findings","Delirium is a common, acute neuropsychiatric syndrome with fluctuating symptoms and frequent underdiagnosis, leading to significant morbidity, mortality, and healthcare burden. This study builds machine learning models using archived electronic health record (EHR) data to predict incident delirium, first training on the MIMIC intensive care database and then applying refined features to non-ICU patients using EHRs from AUBMC. Results compare multiple algorithms and use LIME to enhance interpretability and clinical applicability.","Neuropsychiatric Disease and Treatment downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nNeuropsychiatric Disease and Treatment Dovepress  \nopen access to scientific and medical research  \n Open Access Full Text Article ORIGINAL RESEARCH  \nUsing Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings  \nMariam Heikal 1 , Halim Saad 2 , Pia Maria Ghanime 3 , Tarek Bou Dargham 4 , Maya Bizri 5 , Firas Kobeissy 6 , Wassim El Hajj 1 , Farid Talih 2  \n1Department of Computer Science, American University of Beirut, Beirut, Lebanon; 2Department of Psychiatry, Faculty of Medicine, American University of Beirut, Beirut, Lebanon; 3Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; 4Department of Neurosurgery, Duke University Medical Center, Durham, NC, USA; 5Department of Psychiatry and Psychology, Cleveland Clinic, Cleveland, OH, USA; 6Department of Neurobiology, Morehouse School of Medicine, Atlanta, GA, USA  \nCorrespondence: Mariam Heikal, Department of Computer Science, American University of Beirut, Beirut, Lebanon, [Email mas177@mail.aub.edu](Email mas177@mail.aub.edu)  \n\n| Objective: Delirium is a common and acute neuropsychiatric syndrome that requires timely intervention to prevent its associated morbidity and mortality. Yet, its diagnosis and symptoms are often overlooked due to its variable clinical presentation and fluctuating nature. Thus, in this study, we address the barriers to delirium diagnosis by utilizing a machine learning-based predictive algorithm for incident delirium that relies on archived electronic health records (EHRs) data.\u003Cbr>Methods: We used the Medical Information Mart for Intensive Care (MIMIC) database to create a detailed dataset for identifying delirium in intensive care unit (ICU) patients. Our approach involved training machine learning models on this dataset to pinpoint critical clinical features for delirium detection. These features were then refined and applied to non-ICU patients using EHRs from the American University of Beirut Medical Center (AUBMC) .\u003Cbr>Results: Our study assessed machine learning models like Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Classification and Regression Trees (CART), Random Forest (RF), Neural Oblivious Decision Ensembles (NODE), and Logistic Regression (LR), highlighting superior delirium detection in diverse clinical settings. The CatBoost model excelled in ICU environments with an F1 Score of 89.2%, while XGBoost performed best in general hospital settings with a 75.4% F1 Score. Interpretations using Tabular Local Interpretable Model-agnostic Explanations (LIME) revealed critical indicators such as prothrombin time and hematocrit levels, enhancing model transparency and clinical applicability. These clinical insights help differentiate the delirium predictors between ICU patients, who are often sensitive to various factors.\u003Cbr>Conclusion: The proposed predictive algorithm improves delirium detection rates and streamlines efficiency in hospital electronic systems, thereby enabling prompt interventions to prevent delirium progression and associated complications. The clinical indicators for delirium that we identified in general hospital settings and ICU can greatly help healthcare professionals identify potential causes of delirium and reduce misdiagnosis.\u003Cbr>Keywords: Delirium, ICU delirium, Hospital-acquired delirium, electronic health records, machine learning, clinical indicators |\n| --- |\n| Introduction\u003Cbr>Delirium is a common, acute neuropsychiatric syndrome that is characterized by fluctuating disturbances in inattention, orientation, and cognition and is typically caused by an underlying medical condition or a toxic effect of a substance.1 Despite its transient nature, delirium is associated with poor outcomes, such as longer hospital stays, mechanical ventilation, higher ra","cbCaipwinSuf4Z5g","https://ap.wps.com/l/cbCaipwinSuf4Z5g","pdf",4930307,1,16,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"Why focus on delirium diagnosis using electronic health records?\",\"answer\":\"Delirium often goes unrecognized because its presentation varies and fluctuates, especially in ICU settings. Using archived EHR data enables a predictive approach based on recorded clinical information.\"},{\"question\":\"How were the machine learning models developed and validated?\",\"answer\":\"Models were trained on the MIMIC database to identify key clinical features for delirium detection. The refined features were then applied to non-ICU patients using EHRs from AUB Medical Center.\"},{\"question\":\"Which algorithms performed best in different settings?\",\"answer\":\"CatBoost achieved the highest performance in ICU environments with an F1 score of 89.2%. XGBoost performed best in general hospital settings with an F1 score of 75.4%.\"}]","Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings - Research Findings | PDF",1785674624,40,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"using-machine-learning-and-electronic-health-records-to-identify-neuropsychiatric-risk-scores-for-delirium-in-icu-and-general-hospital-settings-research-findings","",{"@graph":36,"@context":86},[37,54,69],{"@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/using-machine-learning-and-electronic-health-records-to-identify-neuropsychiatric-risk-scores-for-delirium-in-icu-and-general-hospital-settings-research-findings/117240/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",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 focus on delirium diagnosis using electronic health records?","Question",{"text":76,"@type":77},"Delirium often goes unrecognized because its presentation varies and fluctuates, especially in ICU settings. Using archived EHR data enables a predictive approach based on recorded clinical information.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the machine learning models developed and validated?",{"text":81,"@type":77},"Models were trained on the MIMIC database to identify key clinical features for delirium detection. The refined features were then applied to non-ICU patients using EHRs from AUB Medical Center.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithms performed best in different settings?",{"text":85,"@type":77},"CatBoost achieved the highest performance in ICU environments with an F1 score of 89.2%. 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