Hospitalists

Interpretable four-factor day-1 nomogram for predicting sepsis-associated encephalopathy in septic ICU patients with AKI: Development and internal validation in MIMIC-IV.

Sepsis-associated encephalopathy (SAE) is common in the intensive care unit (ICU) and portends worse short- and long-term outcomes. To enable real-time bedside use and multicenter deployment, we aimed to develop a parsimonious, transparent day-1 prediction model using routinely available variables while preserving discrimination, calibration, and clinical utility. Using MIMIC-IV (2008-2022), we co...

Development and explanation of electrocardiogram-based deep learning for predicting short-term mortality in heart failure patients.

BACKGROUND: Heart failure mortality has risen sharply after years of decline, highlighting the limitations of current risk assessment tools in accuracy, complexity, and cost, and the need for improved predictive models. To address this gap, we developed and validated a deep learning model to improve short-term mortality prediction in heart failure patients. METHODS: In this retrospective study, we...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data.

OBJECTIVES: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can...

Architecting Synergy: Knowledge Graphs for Representing Complex, Multi-Domain Patient Information.

Healthcare systems exchange more data than ever, yet gaps in care persist: missed referrals, unsafe polypharmacy, and loss of continuity. This paper a...

Explainable machine learning model based on clinical and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury: a bicentric cohort study.

BACKGROUND: Hematoma expansion or rebleeding after decompressive craniectomy (DC) is a critical determinant of poor prognosis in traumatic brain injur...

Machine Learning in Cirrhosis: Moving From Static Admission Risk to Dynamic Decision Support.

Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation.

BACKGROUND: Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospi...

Dynamic prediction of early discharge after major pancreatic surgery: Derivation and cross-validation of an automatable electronic medical record-based model.

BACKGROUND: Length of hospital admission after major oncologic surgery is often highly variable. Although in carefully selected patients, early discha...

Natural language generation in healthcare: A review of methods and applications.

OBJECTIVE: This study presents a systematic review of natural language generation (NLG) methods and applications in the medical domain, providing quan...

Practical machine learning model for early and accurate prediction of disseminated intravascular coagulation before its progression to an overt stage.

BACKGROUND AND AIMS: In patients with sepsis, anticoagulant therapy is expected to have maximal efficacy when administered before the development of s...

Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study.

Introduction Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, cli...

Enhancing readability and understandability of vascular surgery discharge summaries using artificial intelligence.

ObjectivesThis study aimed to assess whether Large Language Models (LLMs), like ChatGPT-4, could simplify discharge summaries for vascular surgery pat...

Expert Consensus on the Artificial Intelligence Proficiency Competency List and Assessment Framework for Medical Students (2025 Edition).

To cultivate composite medical professionals capable of adapting to the development of intelligent healthcare,this consensus is grounded in the compet...

Synergistic impact of Gram-negative bacterial co-infection on invasive pulmonary aspergillosis in non-neutropenic patients: a machine learning-based risk stratification approach.

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, where coexisting bacterial infections, part...

A randomized controlled trial of artificial intelligence-based analytics for clinical deterioration.

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

A Novel Risk Calculator for Nonhome Discharge after Lower Extremity Bypass.

BACKGROUND: Nonhome discharge (NHD) after lower extremity bypass is associated with discharge delays, increased postdischarge complications, and reduc...

Early heart-rate trajectory phenotypes predict short-term mortality in critically ill patients: a dynamic time-warping cluster analysis.

UNLABELLED: Heart rate (HR) reflects illness severity in critically ill patients, but the prognostic significance of early HR changes is unclear. We a...

A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.

OBJECTIVE: To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a ...

Multisite derivation of a machine learning algorithm using high sensitivity troponin to predict major adverse cardiac events in the emergency department.

OBJECTIVE: To develop a machine learning (ML) algorithm to stratify risk for major adverse cardiac events (MACE) within 30 days in emergency departmen...

Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study.