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Development of a pilot machine learning model to predict successful cure in critically ill patients with community-acquired pneumonia

Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict short-term treatment outcomes in these patients. Short-term pneumonia treatment outcomes are less affected by confounding factors introduced by a prolonged hospital course, and early prediction of short-term treatment outcomes can help physicians identi...

Machine Learning Fairness in Predicting Underweight, Overweight and Adiposity Across Socioeconomic and Caste Group in India: Evidence from the Longitudinal Ageing Study in India

Machine learning (ML) models are widely used to predict body mass index (BMI), yet their fairness across socioeconomic and caste groups remains uncertain, especially in countries with structure inequalities. This study evaluated the accuracy and fairness of ML models in predicting underweight, overweight, and central adiposity, examined the impact of socioeconomic and household factors, identified...

Machine Learning for Paediatric Related Decision Support in Emergency Care – A UK and Ireland Network Survey Study of Emergency Staff

There is great potential for artificial Intelligence (AI) and machine learning (ML) to support decision making in emergency departments (ED), however ...

Not So Fast, I’m Serofast: Using Innovative Education Techniques to Drive Management of People with Syphilis

An increase in syphilis cases in the United States and the global shortage of Benzathine Penicillin G (BPG) calls for evidence-based optimization. Con...

Machine learning models to detect opioid misuse in Emergency Department patients at triage

Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse, but few r...

Effects of Parietal Cathodal tDCS during Game Cue Exposure on Internet Gaming Disorder: A Randomized Double-Blind Sham-Controlled Trial

Internet Gaming Disorder (IGD) is officially listed as a behavioral addiction, exhibits high prevalence and has inadequate treatment efficacy. Targeti...

Predicting coronary artery abnormalities in Kawasaki disease: Model development and external validation

Kawasaki disease (KD) is an acute, pediatric vasculitis associated with coronary artery abnormality (CAA) development. Echocardiography at month 1 pos...

Risk Prediction Modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: Leveraging Machine Learning

Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and bench...

Comparative Analysis of Long COVID and Post-Vaccination Syndrome: A Cross-Sectional Study of Clinical Symptoms and Machine Learning-Based Differentiation

Long COVID is a well-documented post-viral syndrome, while post-vaccination syndrome (PVS) remains poorly characterized. Understanding their similarit...

Neuroimaging Correlates of Post-Stroke Pain After Ischemic Stroke: Secondary Analysis of the INSPiRE-TMS Trial

Post-stroke pain (PSP) affects nearly half of stroke survivors, severely compromising quality of life. The causes of PSP remain underexplored, althoug...

Association between zidovudine and adverse pregnancy outcomes/congenital malformations: A pharmacovigilance study using FAERS data

Zidovudine (AZT), a key antiretroviral drug used for HIV treatment and preventing mother-to-child transmission, has insufficient post-marketing pharma...

Regulating Flexibility for Artificial Intelligence FDA Experience with Predetermined Change Control Plans

Predetermined Change Control Plans (PCCPs) are a recent regulatory innovation by the U.S. Food and Drug Administration (FDA) introduced to enable dyna...

Artificial Intelligence-Guided Molecular Determinants of PI3K Pathway Alterations in Early-Onset Colorectal Cancer Among High-Risk Groups Receiving FOLFOX

Early-onset colorectal cancer (EOCRC), defined as diagnosis before age 50, is rising rapidly and disproportionately affects high-risk populations, par...

Symbolic Regression for Mycophenolic Acid Dosage Prediction in Kidney Transplant Recipients

Chronic kidney disease (CKD) affects millions worldwide and often progresses to end-stage renal disease (ESRD), for which kidney transplantation remai...

Signal Mining and Analysis of Adverse Events of Isotretinoin: 20-year real-world pharmacovigilance analysis based on the FAERS database

To identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (F...

Increasing Value in the Veterans Affairs Healthcare System (VA) with Precision Health: A Continuing Landmark Collaboration with the Department of Energy

By personalizing healthcare to an individual’s specific requirements, precision health promises to maximize benefit and minimize harm, thereby maximiz...

Temperature dominates dengue transmission in Thailand: Machine learning reveals critical thresholds and COVID-19 disruption

Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental a...

Assessment of Medication Adherence in Patients: Development and Validation of a Machine Learning Model

This study addresses limitations of traditional medication adherence assessment tools by developing a machine learning model to evaluate post-discharg...

Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry Data

Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked populati...

Predicting Hospital Admissions Using Pretrained EHR Embeddings: External Evaluation and Insights on Local Vocabulary Adaptation

Unplanned hospital admissions impose substantial strain on healthcare systems, yet predictive models for these events remain underexplored in practice...

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