Emergency and Acute Care Studies

Últimos artículos

• LLMs Anchor on Chief Complaint and Fail to Integrate Evidence in Sequential Clinical Triage

  Dipankar Srirag, Haokai Zhao, Ashutosh Kumar, Eleanor Hopper, Michael Dalton, Quoc Dung Nguyen, Aditya Joshi, Salil S. Kanhere, Padmanesan Narasimhan · 22 de septiembre de 2026

  Triage in the emergency department (ED) is a sequential decision process that unfolds turn by turn. Existing evaluations of large language models (LLMs) for triage use completed retrospective records and report performance close to that of physicians. We implement a methodology for evaluating LLMs o…

• From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Department

  Dipankar Srirag, Aditya Joshi, Salil Kanhere, Padmanesan Narasimhan · 26 de agosto de 2026

  Emergency departments (EDs) operate under time pressure, generating multimodal data such as clinical conversations, triage notes, and discharge documents. Recent advances in natural language processing (NLP), particularly pretrained transformers and large language models, have created new opportunit…

• CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidence

  Guan Qiang, Yushen Chen, Tianlong Liu, David Rotenberg, Ethan H. Kim, Fang Fang · 5 de agosto de 2026

  Emergency triage requires reliable decisions within a short time period. However, the available electronic health record (EHR) data, including structured data and clinical text, are often incomplete, unreliable, and inconsistent. This makes machine learning (ML)-based triage prediction more challeng…

• Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting

  Filipa Lino, B\'arbara Tavares, Carlos Santiago, Cl\'audia Soares, Manuel Marques · 30 de julio de 2026

  Emergency Departments (EDs) are critical access points in healthcare systems, yet they face persistent pressure from unpredictable patient demand, seasonal surges, and non-urgent visits. Effective ED planning requires forecasts at multiple decision-making levels: hospitals need local demand estimate…

• Socioeconomic Inference in LLM Medical Triage: Same Symptoms, Different ZIP Code

  Qi Han Wong · 28 de julio de 2026

  We investigate whether large language models alter medical triage recommendations for identical symptoms when only the patient's socioeconomic status (SES) varies. Using three deployment-tier models (Gemini 3.5 Flash, Claude Sonnet 4.6, GPT-5.4-mini), we hold a single neurological symptom profile fi…

• Multimodal Attention-based Deep Learning for Emergency Triage with Electronic Health Records

  Hazqeel Afyq Athaillah Kamarul Aryffin, Kamarul Aryffin Baharuddin, Mohd Halim Mohd Noor · 21 de julio de 2026

  Accurate emergency triage decision is critical to avoid clinical deterioration, morbidity, and mortality. Machine learning-based triage system involves acquiring the main presenting complaint in text form and assessing vital signs in numerical data, enabling an automated and efficient analysis of pa…

• Proactive Inpatient Bed Requests for Emergency Department Admissions

  QIan Cheng, Nilay Tanik Argon, Aniruddhan Ganesaraman, Serhan Ziya · 20 de julio de 2026

  Emergency department (ED) boarding occurs when admitted patients remain in the ED while awaiting inpatient beds. Boarding is a major driver of ED crowding and has been associated with poor patient outcomes. We propose a framework to help EDs reduce boarding time and length of stay by using informati…

• Iy\`aw\'oBench: A Benchmark for Evaluating Large Language Model Clinical Triage Accuracy on Undifferentiated Febrile Illness in Nigerian Primary Health Settings

  Anthonio Oladimeji Gabriel, Dimeji Abdulsobur Olawuyi, Oloruntoba Ajayi, Temiloluwa Aderemi · 25 de mayo de 2026

  Background. Undifferentiated febrile illness is the leading cause of primary care outpatient visits in Nigeria, yet no validated benchmark exists for evaluating large language model (LLM) clinical triage reasoning in West African primary health settings. Methods. We introduce Iy\`aw\'oBench v1.0, a …

• An Integrated Forecasting Prototype for Emergency Department Boarding Time to Support Proactive Operational Decision Making

  Orhun Vural, Abdulaziz Ahmed, Ferhat Zengul, James Booth, Bunyamin Ozaydin · 20 de mayo de 2026

  Overcrowding in emergency departments (ED) remains a persistent operational challenge worldwide, causing delays in care delivery and downstream congestion. ED boarding time, defined as the duration admitted patients remain in the ED while awaiting inpatient bed placement, is a key indicator of this …

• Domain-Adapted Small Language Models for Reliable Clinical Triage

  Manar Aljohani, Brandon Ho, Kenneth McKinley, Dennis Ren, Xuan Wang · 30 de abril de 2026

  Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-text triage documentation contributes to mistriage and workflow inefficiencies. This study evaluates whether open-source small language models (SLMs) …

• Improving Pediatric Emergency Department Triage with Modality Dropout in Late Fusion Multimodal EHR Models

  Tyler Yang, Romal Mitr · 14 de abril de 2026

  Emergency department triage relies heavily on both quantitative vital signs and qualitative clinical notes, yet multimodal machine learning models predicting triage acuity often suffer from modality collapse by over-relying on structured tabular data. This limitation severely hinders demographic gen…

• Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing

  KMA Solaiman, Joshua Sebastian, Karma Tobden · 25 de febrero de 2026

  Emergency triage decisions are made under severe information constraints, yet most data-driven deterioration models are evaluated using signals unavailable during initial assessment. We present a leakage-aware benchmarking framework for early deterioration prediction that evaluates model performance…

• Early predicting of hospital admission using machine learning algorithms: Priority queues approach

  Jakub Antczak, James Montgomery, Ma{\l}gorzata O'Reilly, Zbigniew Palmowski, Richard Turner · 23 de enero de 2026

  Emergency Department overcrowding is a critical issue that compromises patient safety and operational efficiency, necessitating accurate demand forecasting for effective resource allocation. This study evaluates and compares three distinct predictive models: Seasonal AutoRegressive Integrated Moving…