Hospitalists

Predicting Unplanned Hospital Readmissions in People with Multiple Long-Term Conditions

The prevalence of multiple long-term conditions (MLTCs) is associated with increased healthcare utilisation and an elevated risk of unplanned 30-day hospital readmission. Existing prediction tools predominantly focus on single-disease cohorts and fail to capture the clinical heterogeneity, polypharmacy, and care complexity characteristic of MLTC populations. Using data from 99,207 UK Biobank (UKBB...

Interpretable machine learning prediction of in-hospital mortality in ICU patients with cancer and sepsis using first-day data: Development using MIMIC-IV and external validation in eICU-CRD

Background: Critically ill patients with cancer and sepsis have high in-hospital mortality, but externally validated prediction models are limited. Objective: To develop and externally validate an interpretable machine learning framework using first-day intensive care data. Methods: We used MIMIC-IV version 3.1 for development and internal validation and eICU-CRD for external validation. Eligible ...

First-24-hour machine learning for 30-day mortality prediction in ICU trauma patients: development in MIMIC-III and cross-database evaluation in MIMIC-IV

ICU trauma patients are clinically heterogeneous, and early mortality risk stratification may support monitoring and resource allocation. We developed...

Developing a Heart Failure Readmission Model From Inpatient Electronic Medical Record Data

Importance: Heart failure readmissions remain common following hospitalization, but accurately identifying which patients will be readmitted after dis...

Reconsidering the case against risk prediction in self-harm: routinely collected health data distinguishes groups at higher and lower risk of adverse outcomes following paracetamol overdose

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict ...

Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks

Federated learning (FL) enables multi-institutional training on clinical text without sharing raw data, but gradient inversion can reconstruct sensiti...

Complexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data

Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The margi...

Tacrolimus variability and creatinine predict readmission after liver transplantation

Unplanned readmissions after liver transplantation occur in over 30% of recipients, yet no validated prediction models exist, and prior observational ...

Automated Multisource Electronic Frailty Index in Acute Ischemic Stroke: Development and Clinical Utility

Background: Frailty is common in acute ischemic stroke (AIS) and predicts poor outcomes, but is not routinely captured in acute stroke care. Manual fr...

EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection

Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing de...

Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST

Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible...

Environmental Drivers of Respiratory Disease: A District Level Analysis

Sri Lanka has experienced a decade of progressive forest degradation and rising atmospheric pollution, yet district-level respiratory admissions have ...

Predicting Mechanical Ventilation Requirement in Guillain-Barre Syndrome using a Multi-Functional Machine Learning Algorithm

Background: To develop and validate multiple Machine Learning (ML) algorithms that predict Mechanical Ventilation (MV) requirement in Guillain-Barre S...

CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping

Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increa...

A Reproducible Clinical Decision-Support Suite on MIMIC-IV

Most published clinical-AI results are single models on a single dataset, difficult to reproduce, and rarely validated outside their training hospital...

Comparative Evaluation of Machine Learning and Deep Learning Models for Early Prediction of Severe Acute Pancreatitis: A Multi-Model Study Using the 2012 Revised Atlanta Classification

**Background:** Acute pancreatitis (AP) is a common gastrointestinal emergency with a subset of patients progressing to severe acute pancreatitis (SAP...

Agentic Artificial Intelligence for Hospital Readmission Review: A Single-Center Blinded Evaluation and Exploratory Qualitative Analysis

Background: Manual review of 30-day hospital readmissions can identify actionable quality and safety problems, but it is labor-intensive. We developed...

A Machine-Learned Comorbidity Index

Traditional comorbidity scores (e.g., Charlson and Elixhauser) are widely used for risk adjustment and patient stratification, but they have two key l...

Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation

Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimat...

A Heterogeneous Graph Neural Network Framework for Multi-Horizon Stroke Mortality Prediction

Background: Machine learning models for stroke mortality prediction typically treat each time horizon independently and use flat tabular features that...