Public Health & Policy

Privacy-Enhancing Sequential Learning under Heterogeneous Selection Bias in Multi-Site EHR Data

To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, without sharing raw individual-level data. We illustrate their utility through a cross-biobank analysis of smoking and 97 cancer subtypes using data from the NIH All of Us (AOU) and the Mich...

Predicting Vaping Cessation in Young Adults: A Machine Learning and Explainable Artificial Intelligence (XAI) Approach to Public Health Intervention

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks. While e-cigarettes can substantially reduce exposure to toxic combustion byproducts and may aid in smoking cessation for adult tobacco users, evidence links e-cigarette use to respiratory and cardiovascular injury, raising concerns about long-term health outcomes in vapers. ...

Discriminating the prodromal stage of multiple sclerosis using longitudinal health administrative claims data and machine learning–based sequence analysis

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. Early detection of the prodromal phase could enable timely inte...

Comprehensive, Transparent, and Fair Machine Learning Models for Hypertension Risk Prediction: Benchmarking With Framingham, External Validation, Individual-Level Analysis, and Equitable Clinical Utility

Hypertension (HTN) is a leading, yet often underdiagnosed, cause of cardiovascular diseases worldwide. While clinical risk scores like the Framingham ...

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record Data

Information from electronic health records (EHRs) may be incorporated into computable phenotype algorithms in efforts to overcome inaccuracies of algo...

Internal and External Validation of Machine Learning Algorithms Versus FINDRISC for Incident Type 2 Diabetes: A Transparent, Explainable Benchmark Using SHAP

Type 2 diabetes mellitus (T2DM) affects almost half a billion people, and the projected cost is $2.25 trillion by 2030; early detection strategies are...

Multidimensional Evaluation of Large Language Models on the AAP In-Service Examination: Assessing Accuracy, Calibration, and Citation Reliability

Large language models (LLMs) have demonstrated rapid advancements in natural language understanding and generation, prompting their integration into b...

BeatAI: BiomEtrics for Atrial Arrhythmia Tracking Using Artificial Intelligence

Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery and is associated with longer hospital stays and adv...

Childhood Maltreatment and Risk for Illicit Substance Use: Evidence for Mid-Adolescence as a Sensitive Exposure Period

Childhood maltreatment is a well-established risk factor for substance misuse. However, it remains unclear whether risk for specific illicit substance...

Intelligent Decision Support System Facilitating Early Detection of Cardiovascular Disease

Cardiovascular disease (CVD) remains the primary cause of mortality worldwide, with higher fatality rates in India. Multi-modal diagnostics integratin...

Demographic Drivers of Epidemic Outcomes: Sensitivity Analysis of Multidimensional Parameters in the Covasim Model

Sensitivity analysis is a key tool for identifying which model inputs most strongly influence model outputs thereby informing data collection prioriti...

From claims to care: Machine learning algorithm to classify urinary tract infection cases using Swiss health insurance data

To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibioti...

A Hybrid Deep Learning-Mechanistic Modeling Framework for Dengue Transmission Dynamics in Guangdong, China

Dengue fever is a mosquito-borne viral disease with strong seasonality, periodicity, and spatial heterogeneity, posing a persistent global public heal...

Urinary peptidomic signatures predict overall and progression-free survival in patients with bladder cancer

Clinicopathologic calculators for bladder cancer moderately predict survival and fail to depict the underlying molecular phenotype. We applied urinary...

A Prospective Real-time Early Warning System to Anticipate Onsets and Peaks of Respiratory Diseases Outbreaks at the State Level in the U.S. A Transfer Learning Approach Leveraging Digital Traces

Respiratory disease outbreaks burden U.S. healthcare systems with over one million hospitalizations annually, yet current surveillance systems lag 1-2...

Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis

There is a lack of automated pipelines for diagnostic classification of point-of-care tests for neglected tropical diseases. Here we present an end-to...

Falls in Assisted Living Facilities: Can AI improve documentation and reduce injury?

Falls among elderly residents in assisted living facilities (ALFs) are prevalent, costly, and frequently under-documented. AUGi, a wall-mounted device...

Advancing cardiovascular disease risk prediction beyond conventional methods: a systematic review of multimodal machine learning models integrating traditional clinical factors and multi-omics data

Cardiovascular disease (CVD) is a leading global health burden. Traditional risk prediction models, though widely used, often overlook genetic predisp...

Modelling Approaches for Predicting the Distribution of Skin NTDs: A Systematic Review

Skin neglected tropical diseases (NTDs) such as cutaneous leishmaniasis, lymphatic filariasis, mycetoma, and podoconiosis affect millions in endemic r...

Open-source solution for evaluation and benchmarking of large language models for public health

Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing tasks, including text classification, in...