Critical Care

Transformer-based artificial intelligence on single-cell clinical data for homeostatic mechanism inference and rational biomarker discovery

Artificial intelligence (AI) applied to single-cell data has the potential to transform our understanding of biological systems by revealing patterns and mechanisms that simpler traditional methods miss. Here, we develop a general-purpose, interpretable AI pipeline consisting of two deep learning models: the Multi- Input Set Transformer++ (MIST) model for prediction and the single-cell FastShap mo...

Suitability of just-in-time adaptive intervention in post-COVID-19-related symptoms: A systematic scoping review

Patients with post-COVID-19-related symptoms require active and timely support in self-management. Just-in-time adaptive interventions (JITAI) seem promising in meeting these needs, as they aim to provide tailored interventions based on patient-centred measures. This systematic scoping review explores the suitability and examines key components of a potential JITAI in post-COVID-19 syndrome. Datab...

PanEcho: Complete AI-enabled echocardiography interpretation with multi-task deep learning

Echocardiography is a cornerstone of cardiovascular care but relies on expert interpretation and manual reporting from a series of videos. We propose ...

Understanding the Feasibility of Computer Vision in Diagnosing Respiratory Infections in Pediatric Emergency Rooms

Respiratory infections are a leading cause of pediatric emergency visits globally, requiring timely and accurate assessment. This study evaluated the ...

A Novel Hybrid LSTM-DNN Model for Ventilator Pressure Prediction: Comparative Analysis of Data Splitting Strategies

In recent years, deep learning has significantly transformed ventilator pressure forecasting, which is crucial for providing patients with safe, perso...

Integrating a host transcriptomic biomarker with a large language model for diagnosis of lower respiratory tract infection

Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as ...

Machine Learning-Based Mortality Prediction in Critically Ill Patients with Hypertension: Comparative Analysis, Fairness, and Interpretability

Hypertension is a leading global health concern, significantly contributing to cardiovascular, cerebrovascular, and renal diseases. In critically ill ...

Machine learning-based short-term forecasting of COVID-19 hospital admissions using routine hospital patient data

During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmiss...

Artificial intelligence-enhanced Electrocardiography Score for Perioperative Risk Assessment in Non-cardiac Surgery

The role of electrocardiography (ECG) has been limited in the preoperative risk evaluation in noncardiac surgery due to its low prognostic value. Rece...

Creation of an Open-Access Lung Ultrasound Image Database For Deep Learning and Neural Network Applications

Lung ultrasound (LUS) offers advantages over traditional imaging for diagnosing pulmonary conditions, with superior accuracy compared to chest X-ray a...

Retrospective Machine Learning Approach for Forecasting In-Hospital Death in ICU Patients After Cardiac Arrest

Accurate identification of patients at high risk of in-hospital mortality in intensive care units (ICUs) is vital for enhancing clinical decision-maki...

Beyond episodic early warning systems: a continuous clinical alert system for early detection of in-hospital deterioration

Efficient patient monitoring on the medical-surgical wards is crucial to prevent significant in-hospital adverse events. Standard episodic inpatient a...

Harnessing Artificial Intelligence for Accurate Diagnosis and Radiomics Analysis of Combined Pulmonary Fibrosis and Emphysema: Insights from a Multicenter Cohort Study

Combined Pulmonary Fibrosis and Emphysema (CPFE), formally recognized as a distinct pulmonary syndrome in 2022, is characterized by unique clinical fe...

Contextualized Biomedical Language Processing Enhances ICU Survival Prediction

Cerebrospinal fluid (CSF) culture is the diagnostic gold standard for neuroinfectious diseases such as bacterial meningitis, but its sensitivity is li...

AI-Enabled Diagnostic Prediction within Electronic Health Records to Enhance Biosurveillance and Early Outbreak Detection

Detecting infectious disease outbreaks promptly is crucial for effective public health responses, minimizing transmission, and enabling critical inter...

Activation status of immune cells in the airway is a defining feature of severe fungal asthma

Airborne fungi are potent inducers of respiratory disease and cause the debilitating conditions severe asthma with fungal sensitisation (SAFS) and all...

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability

Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that places a substantial burden on healthcare systems w...

Rural Medical Centers Struggle to Produce Well-Calibrated Clinical Prediction Models: Data Augmentation Can Help

Machine learning models support many clinical tasks; however, challenges arise with the transportability of these models across a network of healthcar...

Auditor Models to Suppress Poor AI Predictions Can Improve Human-AI Collaborative Performance

Healthcare decisions are increasingly made with the assistance of machine learning (ML). ML has been known to have unfairness – inconsistent outcomes ...

Assessment and Prediction of Clinical Outcomes for ICU-Admitted Patients Diagnosed with Hepatitis: Integrating Sociodemographic and Comorbidity Data

Hepatitis, a disease characterized by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million deaths annu...