Critical Care

Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models

Accurate prediction of mortality in critically ill patients with hypertension admitted to the Intensive Care Unit (ICU) is essential for guiding clinical decision-making and improving patient outcomes. Traditional prognostic tools often fall short in capturing the complex interactions between clinical variables in this high-risk population. Recent advances in machine learning (ML) and deep learnin...

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a severe hematologic disorder marked by pancytopenia and bone marrow failure. ICU admission often reflects disease progression or complications requiring critical care. Predicting short-term survival in these patients is vital for individualized treatment and resource optimization. Nomograms provide a practical tool for integrating clinical parameters, offering accurate visualiz...

Unbiased multi-omics network-based data integration allows clinically relevant outcome-predicting clustering of individuals with heart failure

Heart failure is a multifaceted clinical syndrome, in which the heart fails to supply adequate blood to meet the body’s oxygen and nutrients needs. Ev...

The impact of evaluation strategy on sepsis prediction model performance metrics in intensive care data

The prediction of the onset of sepsis, a life-threatening condition resulting from a dysregulated response to an infection, is one of the most common ...

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records

Electronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic clinical decisions but are often unsuited for advan...

Plasma proteomics for novel biomarker discovery in childhood tuberculosis

Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Curren...

Development and Validation of an Artificial Intelligence Predictive Model to Accelerate Antibiotic Therapy for Critical Ill Children with Sepsis in the Pediatric ED with Pediatric ICU Disposition

Pediatric sepsis accounts for over 72,000 US hospitalizations annually with significant mortality and morbidity. Many pediatric hospitals struggle to ...

Quality of Human Expert vs. Large Language Model Generated Multiple Choice Questions in the Field of Mechanical Ventilation

Mechanical ventilation (MV) is a critical competency in critical care training, yet standardized methods for assessing MV-related knowledge are lackin...

A Deep Learning–Based Automated Detection of Mucus Plugs in Chest CT

This study presents a novel two stage deep learning algorithm for automated detection of mucus plugs in CT scans of patients with respiratory diseases...

Artificial Intelligence (AI) Models for Cardiovascular Disease Risk Prediction in Primary and Ambulatory Care: A Scoping Review

Mortality from cardiovascular disease (CVD) has seen a dramatic increase over the past decades, which has led to a significant increase in the develop...

Unravelling the Complex Inflammatory Landscape of COVID-19 infection: A Pathway to Biomarkers Identification in Infection-Associated Delirium in the ICU

Delirium is a serious complication in patients with COVID-19-related acute respiratory distress syndrome (ARDS) admitted to the intensive care unit (I...

Predicting in-hospital indicators from wearable-derived signals for cardiovascular and respiratory disease monitoring: an in silico study

Cardiovascular and respiratory diseases (CVRD) are the leading causes of death worldwide. The construction of health digital twins for patient monitor...

Evaluating biomedical feature fusion on machine learning’s predictability and interpretability of COVID-19 severity types

Accurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow. Current techniqu...

Open-source computational pipeline automatically flags instances of acute respiratory distress syndrome from electronic health records

Physicians, particularly intensivists, face information overload and decision fatigue, underscoring the need for automated diagnostic tools. Acute Res...

Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines

The analysis of complex biomedical datasets is becoming central to understanding disease mechanisms, aiding risk stratification and guiding patient ma...

Profile of deaths mentioning ischemic and hemorrhagic stroke in Brazil: a population-based machine learning analysis

Brazil has the highest stroke rates in Latin America. The aim of this study was to investigate the profile of deaths mentioning stroke in Brazil betwe...

Development and Prospective Implementation of a Large Language Model based System for Early Sepsis Prediction

Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve pat...

Predictive Modeling of Heart Failure Readmissions

Federal programs to mitigate hospital readmission of patients with heart failure (HF) monetarily encourage hospitals through the use of penalties. The...

Predicting 28-Day Mortality in First-Time ICU Patients with Heart Failure and Hypertension Using LightGBM: A MIMIC-IV Study

Heart Failure (HF) and Hypertension (HTN) are common yet severe cardiovascular conditions, both of which significantly increase the risk of adverse ou...

NTDscope: A multi-contrast portable microscope for disease diagnosis

Accurate diagnostics are essential for disease control and elimination efforts. However, access to diagnostics for neglected tropical diseases (NTDs) ...