Primary Care

Machine learning models for reducing false positives in Fluorometric newborn screening of phenylketonuria: Development, validation, and clinical application.

BACKGROUND: Fluorometric newborn screening for phenylketonuria (PKU) is widely used but plagued by high false positives (FPs), leading to unnecessary follow-ups and parental anxiety. Machine learning (ML) models may optimize screening efficiency by reducing FPs while preserving sensitivity. METHODS: A retrospective study used fluorometric PKU screening data from 493,652 neonates (2022-2024) at She...

Beyond clinical predictors: Determining the impact of psychosocial factors in chronic pain after spinal cord injury.

UNLABELLED: ▒: Although chronic pain is common after traumatic SCI, prognostic models have traditionally prioritized clinical injury characteristics, and the relative contribution of sociodemographic and psychological factors remains under-characterized. METHODS: Secondary analysis of a national longitudinal cohort from United States inpatient rehabilitation centers participating in the Spinal Cor...

[Artificial intelligence in diabetic retinopathy screening].

INTRODUCTION: Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological dat...

Prediction of low birth weight using machine learning-based analysis of environmental and maternal risk factors: insights from the Korean CHildren's ENvironmental health Study (Ko-CHENS).

Adverse birth outcomes such as low birth weight (LBW) increase the risk of metabolic disorders and hypertension later in life. Although previous studi...

Development of Machine Learning-Driven Dual-Task Gait Test Model for Cognitive Impairment Screening.

OBJECTIVE: To develop an artificial intelligence (AI)-aided dual-task gait test model for scalable, high-throughput cognitive impairment screening. DE...

Machine learning and SHAP interpretation for predicting coronary heart disease-diabetes comorbidity with dietary antioxidants.

Coronary heart disease (CHD) and diabetes mellitus frequently co-occur through shared mechanisms such as oxidative stress and inflammation. Whether sp...

Machine learning-based screening model for Tic disorders comorbid with attention-deficit/hyperactivity disorder in children and SHAP value interpretation: a retrospective observational study.

Tic disorders (TD) are common neurodevelopmental conditions in children and adolescents, characterized primarily by motor and vocal tics, with heterog...

Quantum-safe cyber digital twin architectures for proactive threat forecasting and adaptive defense.

As the recent developments in digital infrastructures have led to increased risk of cyber threats. Among them, quantum technology assisted threats sig...

Explainable ensemble machine learning for predicting diabetes mellitus and identifying key risk factors: a population-based study in northern Bangladesh.

Diabetes mellitus (DM) is an escalating global public health concern, with a rapidly increasing burden in low- and middle-income countries, including ...

Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.

BACKGROUND: The exponential growth of scientific publications has increased the complexity of evidence synthesis. Systematic reviews remain essential ...

Electronic health record-derived machine learning model for hypoglycemia risk prediction in type 2 diabetes mellitus patients: development and validation.

BACKGROUND: Hypoglycemia is a serious complication of diabetes. Early recognition of hypoglycemia can improve clinical prognosis, however, traditional...

Automated evidence surveillance with AI-enabled preranking with cutoff in living guideline maintenance: a simulation study.

OBJECTIVES: Living guidelines are an emerging approach to ensure timely synthesis of the research evidence. However, pragmatic methods for maintenance...

Repurposing cardiovascular disease prediction models for cancer.

INTRODUCTION: Population cancer screening detects the presence of early-stage disease rather than assessing future disease risk. We evaluated whether ...

Association between the development of sepsis and the BAR index in patients with acute pancreatitis: A retrospective investigation based on MIMIC-IV database.

Sepsis, as a severe complication of acute pancreatitis (AP), needs to be identified and treated as early as possible. The blood urea nitrogen-to-album...

Application of construction and validation of logistic regression model in risk prediction of non-proliferative diabetic retinopathy in type 2 diabetes mellitus.

AIMS: Diabetic retinopathy (DR) is a leading cause of vision impairment in type 2 diabetes mellitus (T2DM), with non-proliferative DR (NPDR) represent...

Integration of network pharmacology, deep learning, and molecular biology reveals the efficacy of Citrus aurantium L. var. amara Engl. blossom extract in ameliorating diabetic osteoporosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic osteoporosis (DOP) is a type of metabolic bone disease. Blossom of Citrus aurantium L. var. amara Engl. (CAVA...

Multi-task deep learning for sub-clinical screening of mood and sleep disturbances using physical activity biomarkers.

BACKGROUND: Sub-clinical screening for mood disorders and sleep disturbances remains challenging due to limited accessibility of mental health screeni...

European Science for Health Research Needs and Priorities.

The European research landscape for developing new diagnostic, preventive, and therapeutic interventions is fraught with challenges. Scarcity of quali...

External Validation of a mHealth Tool for Detecting Gingival Inflammation in Community-Dwelling Older Adults.

OBJECTIVE: This study aimed to externally validate the screening performance of a pro-social (equitable, accessible), explainable AI (XAI)-guided mobi...

A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme implicated in cancer immune escape and remains an attractive therapeutic target despi...