Federated Learning Using Model Projection for Multi-Center Disease Diagnosis with Non-IID Data

Federated Learning Using Model Projection for Multi-Center Disease Diagnosis with Non-IID Data

Information Science, Artificial Intelligence, Medicine, Environmental and Public Health,
Federated Learning   Model Projection   Multi-Center Disease Diagnosis   Non-IID Data   Catastrophic Forgetting   Invalid Aggregation  

Federated Learning Using Model Projection for Multi-Center Disease Diagnosis Background Introduction With the rapid development of medical imaging technology, research on automated diagnostic methods has shown good performance on single-center datasets. However, these methods often find it difficult to generalize to data from other healthcare facil...

Electronic Health Record Signatures Identify Undiagnosed Patients with Common Variable Immunodeficiency Disease

Electronic Health Record Signatures Identify Undiagnosed Patients with Common Variable Immunodeficiency Disease

Medicine, Rheumatology and Immunology, Environmental and Public Health,
Common Variable Immunodeficiency   Electronic Health Records   Machine Learning Algorithm   Immune System   Disease Diagnosis   Artificial Intelligence   Phenotype  

Utilizing Electronic Health Record Features to Identify Undiagnosed Patients with Common Subtype of Immunodeficiency Recently, Johnson and colleagues published a study titled “Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease” in Science Translational Medicine. This research utilizes ele...

Strokeclassifier: Ischemic Stroke Etiology Classification by Ensemble Consensus Modeling Using Electronic Health Records

Information Science, Artificial Intelligence, Medicine, Environmental and Public Health, Neurology,
stroke   machine learning   artificial intelligence   etiology classification   health records   diagnostic accuracy   secondary prevention  

StrokeClassifier: An AI Tool for Etiological Classification of Ischemic Stroke Based on Electronic Health Records Project Background and Motivation Identifying the etiology of strokes, particularly acute ischemic stroke (AIS), is crucial for secondary prevention, but it is often very challenging. In the United States, there are nearly 676,000 new c...

Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Information Science, Artificial Intelligence, Medicine, Environmental and Public Health, Humanities and Others,
large language models   social determinants of health   electronic health records   natural language processing   model optimization   synthetic data generation  

Using Large Language Models to Identify Social Determinants of Health from Electronic Health Records Background and Research Motivation Social Determinants of Health (SDOH) have a significant impact on patient health outcomes. However, these factors are often incompletely recorded or missing in the structured data of Electronic Health Records (EHR)...

Medical History Predicts Phenome-Wide Disease Onset and Enables the Rapid Response to Emerging Health Threats

Information Science, Artificial Intelligence, Medicine, Environmental and Public Health,
medical history   disease prediction   rapid response   COVID-19   neural network   risk stratification  

Using Medical Records to Predict Common Disease Incidence and Support Rapid Response to Emerging Health Threats Research Background and Motivation The COVID-19 pandemic exposed systemic issues and a lack of data-driven guidance globally, significantly affecting the identification of high-risk populations and pandemic preparedness. Assessing individ...