Primary Care

A Large-Scale Serum Metabolite Panel for Baseline Detection of Alzheimer’s Disease

Blood-based metabolomic signatures offer promising, non-invasive avenues for Alzheimer’s disease (AD) detection. We aimed to identify a serum metabolite panel integrated with APOE ε4 status for distinguishing AD from cognitively normal (CN) individuals. Baseline data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed for 594 participants (237 AD, 357 CN). High-resolution ser...

A Tabular Residual Neural Network for Diabetes Classification and Prediction

Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia, with type 1 characterized as an autoimmune destruction of pancreatic beta cells and type 2 characterized by insulin resistance with progressive beta cell dysfunction. This study applied an existing binary classification algorithm (ALTARN) to accurately predict DM. ALTARN, as a tabular attention residual neural network, ...

Traditional Machine Learning Outperforms Automated Machine Learning for Postpartum Readmission Prediction: A Comprehensive Performance and Health-Economic Analysis

Automated machine learning (AutoML) promises to democratize predictive modeling in healthcare by automating algorithm selection and hyperparameter opt...

An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare Study

Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure cases in the United States and remains a diagnostic...

Advancing Cardiovascular Disease Diagnosis with an Interpretable and Responsible AI Framework

Cardiovascular disease (CVD) remains a leading global health threat, responsible for one in five deaths worldwide. Early detection is critical to miti...

Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment

Cognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offe...

Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden

Perinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying a...

Machine Learning Prediction of Blood Pressure Control in Patients With Hypertension and Heart Failure Using Longitudinal Clinical Data

To develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal B...

Dynamic Stroke Risk Stratification via Machine Learning: A Multi-Level Single-Center Study

Stroke is a leading global public health challenge and the second leading cause of death worldwide. In China, its burden continues to escalate amid po...

A preregistered, Open Pipeline for Early Cerebral Palsy Risk Assessment from Infant Videos

Cerebral Palsy (CP), affecting approximately 1 in 500 children due to abnormal brain development, impacts movement control. Early risk assessment via ...

Opportunistically Detecting Signs of Hypertension on a Consumer Smartwatch

Hypertension is a silent killer, with over half of affected adults unaware of their condition1,2. This lack of awareness is a major concern, as early ...

Leveraging Pretrained Large Language Model for Prognosis of Type 2 Diabetes Using Longitudinal Medical Records

Timely prognosis of type 2 diabetes (T2D) is critical for effective interventions and reducing economic burden. Longitudinal medical records offer pot...

Early Detection of Cardiovascular Disease Risk Using Multi-Parameter Biomarker Analysis and Machine Learning: A Prospective Cohort Study

Cardiovascular disease (CVD) remains the leading cause of mortality globally, with many events occurring in individuals without prior diagnosed condit...

Detecting Mental Disorders in Social Media Using a Transformer-Based Ensemble of Binary Classifiers

This study introduces a novel transformer-based ensemble framework for the multi-label detection of mental health disorders from social media posts. U...

Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on CT scans

The distribution of abdominal adipose depots and their mechanistic links to type 2 diabetes remain incompletely understood. This study elucidated the ...

Temporal deep learning with clinically engineered biomarkers for the early prediction of type 2 diabetes

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early i...

Evaluation of T2DM Phenotyping Using Optimized Retrieval-Augmented Generation (RAG) and the Impact of Embedding Model, Context, and Prompt

Identification of patient cohorts from EHRs is challenging because ICD codes primarily serve billing and may misrepresent disease status, while key in...

DRB1 Subtyping Reveals Divergent Risk and Protection for Type 1 Diabetes in Middle Eastern Populations

Type 1 diabetes (T1D) is strongly influenced by HLA variation, yet current genetic risk models developed largely in European cohorts perform suboptima...

Evaluation of SSI risk prediction model after spinal surgery: A systematic review and critical appraisal

This study aimed to systematically review and critically evaluate the risk of bias and applicability of surgical site infection (SSI) risk prediction ...

Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project

Develop and deploy a real-time, EHR-integrated machine learning phenotype to identify emergency department (ED) patients with opioid use disorder (OUD...