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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 n...

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 decis...

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 ...

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 ...

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 lif...

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 tr...

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 D...

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 dise...

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 ...

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 driv...

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 ...

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 s...

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...

A systematic review of early neuroimaging and neurophysiological biomarkers for post-stroke mobility prognostication

Accurate prognostication of mobility outcomes is essential to guide rehabilitation and manage patien...

Machine Learning-Driven Decision-Support System for Nursing Risk Assessment in Post-Discharge Care: A Design Science Approach

Hospital readmissions represent a persistent challenge for healthcare systems, often stemming from i...

Mining medical narratives on geriatric falls to predict post-fall hospitalization via survival models and large language models

Timely admission to the emergency department is a crucial determinant of patient outcomes. Conversel...

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