Critical Care

Association of lactate-to-albumin ratio with 28-day mortality in elderly sepsis patients: a retrospective MIMIC-IV database analysis.

BACKGROUND: The lactate-to-albumin ratio (LAR) is correlated with mortality in critically ill patients; however, its predictive value for sepsis in the elderly remains underexplored. METHODS: This retrospective cohort study used the data from the MIMIC-IV database. The primary outcome was 28-day ICU mortality in elderly patients with sepsis. We used multivariate logistic regression, restricted cub...

Construction of a depression risk prediction model for hepatitis B patients based on machine learning strategy.

BACKGROUND: Hepatitis B (HBV) is a chronic viral infection that can lead to cirrhosis, liver failure, and liver cancer, and has a profound impact on the patient's mental health. However, current depression screening mainly relies on self-filled scales and clinical experience, lacking objective and efficient prediction tools. This study aims to construct a risk prediction model for depression in he...

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...

Development and Validation of a Machine Learning Model That Uses Voice to Predict Aspiration Risk.

BACKGROUND: Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter-...

A multi-view neural framework with attention for epileptic seizure classification.

Objective. Epilepsy is a chronic brain disorder characterized by recurrent seizures due to abnormal neuronal firing. Electroencephalogram (EEG)-based ...

K-MIMIC: a nationwide Korean multi-institutional Multimodal intensive care dataset.

BACKGROUND: Recent advancements in critical care have highlighted the need for comprehensive, multimodal datasets to support clinical decision-making ...

Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.

BACKGROUND: Conventional machine learning (ML) models for predicting surgical outcomes have limitations in generalizability We explored large language...

Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRI.

BACKGROUND: Fontan-associated liver disease (FALD) is associated with morbidity and mortality in patients with palliated single ventricle congenital h...

An Explanation User Interface for Artificial Intelligence-Supported Mechanical Ventilation Optimization for Clinicians: User-Centered Design and Formative Usability Study.

BACKGROUND: The integration of artificial intelligence (AI) into clinical decision support systems (CDSSs) for mechanical ventilation in intensive car...

Detection of Swallowing Abnormalities in Pediatric FEES Recordings Using Rule-Based and Model-Based Methods.

Pediatric swallowing dysfunction (SwD) poses serious health risks, including aspiration, malnutrition, and recurrent respiratory infections, making ea...

Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction.

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine learning algorithms to predict the risk of dementia...

Interpretable machine learning reveals phosphorus-to-albumin ratio as a novel predictor of mortality and acute kidney injury in critically ill pancreatitis patients: a multi-center retrospective analysis.

Acute kidney injury (AKI), a common and severe complication of acute pancreatitis (AP), is amenable to early intervention. Phosphorus-to-albumin ratio...

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Postoperative delirium is a frequent and serious complication lacking effective prediction tools for general ward patients. This study aimed to identi...

Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.

Accurate and timely stroke-risk prediction is necessary to help patients at risk take guided measures, as stroke remains a leading cause of death and ...

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance.

OBJECTIVE: Many healthcare problems involve complex patient trajectories represented as Multivariate Time Series (MTS), with predictions often coming ...

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 ...

Ensemble transfer learning for classifying physical examinations in GP consultation: a multi-model approach to human-object and human-to-human activity recognition.

OBJECTIVES: This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep lear...

Multi-omics and machine learning-based profiling of severity signatures in Mycoplasma pneumoniae infection in children.

Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory infection in children; however, the mechanisms driving its progression to severe disease...

Timing matters: a machine learning-driven comparison of community and hospital-onset sepsis.