Primary Care

Identification of replication factor C subunit 4 as a potential therapeutic target in esophageal squamous cell carcinoma based on bioinformatic analysis and machine learning.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the highly lethal and aggressive malignant tumors worldwide. To effectively prevent and treat this disease, the search for novel molecular targets is of great significance for promoting the molecular diagnosis and targeted therapy of ESCC. METHOD: Gene expression profiles from gene expression omnibus (GEO) datasets were normalized and...

Real‑World Clinical Characterization of Major Depressive Disorder and Treatment‑Resistant Depression Supported by Natural Language Processing: Multicenter Observational Study From the MOOD Project.

BACKGROUND: Major depressive disorder (MDD) and treatment-resistant depression (TRD) are heterogeneous conditions in which key clinical details are split across structured fields and free-text notes in electronic health records (EHRs), constraining population-level insight and timely audit of care quality. OBJECTIVE: This study aims to present a clinician-oriented, artificial intelligence-supporte...

Effect of a Personalized Mobile Health Intervention Using Artificial Intelligence (the WARIFA App) Versus a Nonpersonalized Intervention on User-Defined Objectives, Healthy Lifestyles, and Management of Type 1 Diabetes (T1D): Protocol for a Randomized Controlled Trial.

BACKGROUND: Noncommunicable diseases are the leading cause of death worldwide. Cardiovascular and respiratory diseases, cancer, and type 2 diabetes sh...

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and vari...

Using Machine Learning Methodology to Identify Predictors of Non-Response to MTSS-B: a Focus on Discipline Problems and Juvenile Justice Involvement.

Multi-tiered systems of support for behavior (MTSS-B) is a widely-used tiered preventive intervention currently used in over 25,000 schools across the...

Understanding Blood-Injection-Injury Phobia: Do Not Miss the Point.

Needle and blood-injection-injury phobia is commonly encountered in the perioperative setting. It can significantly disrupt operating room throughput,...

A hybrid BiLSTM and rule-based system for integrated diabetes prediction and personalized guidance.

Diabetes is a common chronic disease that needs early diagnosis and proper management to avoid severe complications. While current Artificial Intellig...

Integrative transcriptomic analysis identifies immune-associated candidate genes and altered immune cell infiltration in pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a progressive vascular disease characterized by immune dysregulation and pulmonary vascular remod...

Lung Cancer Screening and Prevention: Beyond Conventional Strategies in a Rapidly Changing Global Milieu.

Lung cancer remains the leading cause of cancer-related mortality worldwide despite advances in early detection and treatment. Furthermore, its epidem...

Diabetic retinal disease cure accelerator: Modernizing staging and endpoints.

The "Diabetic Retinal Disease (DRD) Cure Accelerator," a joint initiative by the Mary Tyler Moore Vision Initiative and the Collaborative Community on...

Finding Associative and Causal Effects of Temporal Changes in Health Features for Prevalent and Incident Cancer in Males: A Machine Learning Approach.

Cancer remains a major public health challenge driven by complex interactions among sociodemographic, behavioral, clinical, and environmental factors....

Integrating Anomaly Detection and LLM-Based Explanation Generation in Clinical Data Dashboards.

This paper presents an integrated approach that combines unsupervised anomaly detection with large language model (LLM)-based explanation generation t...

Prediction of Early Functional Outcome After Acute Ischemic Stroke Using Real-World Clinical Data in Vietnam and Indonesia: Retrospective Cohort Study.

Accurate prediction of early functional outcome after acute ischemic stroke is critical for clinical decision-making. This retrospective cohort study ...

Artificial Intelligence-Based Prediction of Progression from Gestational Diabetes to Type 2 Diabetes.

Women with a history of gestational diabetes mellitus (GDM) are at elevated risk of developing type 2 diabetes mellitus (T2DM) postpartum. This study ...

Machine Learning Applications Within the Earlier Medicine Framework for Stroke: A Scoping Review.

This scoping review explores how machine learning (ML) has been applied to stroke research within the Earlier Medicine framework, which promotes proac...

A Prediction Model for Cardiovascular Death in Individuals with Prediabetes.

We developed a machine learning model to estimate the personalized risk of cardiovascular (CV) death within 5-years among obese/overweight people with...

Integrating Nutritional Status in Machine Learning Predictive Models for Cardiovascular Risk: A Pilot Study.

This study explored the use of machine learning (ML) models for cardiovascular risk stratification in an elderly Thai population. A cross-sectional an...

Hybrid AI Decision Support in Healthcare: Insights into Usability and Trust.

Decision Support Systems (DSS) increasingly integrate Artificial Intelligence (AI) to enhance clinical reasoning, yet adoption depends on professional...

Identifying Clinical Predictors of Diabetes and Prediabetes: An Explainable AI Approach Using Primary Care Electronic Medical Records.

Prediabetes (PD), a reversible metabolic condition that precedes type 2 diabetes (T2DM), carries a high risk of progression to T2DM, but can be effect...

MINE: An Interactive Platform for Expert-Guided Medical Information Extraction.

Clinical reports contain valuable patient information but are difficult to use due to their unstructured format and privacy constraints. We present MI...