Research on Medical Package Recommendation Systems: Heterogeneous Graph Neural Network Based on Dual-Level Interaction Awareness With the widespread application of electronic health records (EHRs) in the medical field, how to mine potential and valuable medical knowledge to support clinical decision-making has become an important research direction...
Application of Knowledge-Enhanced Graph Topic Transformer in Interpretable Biomedical Text Summarization Research Background Due to the continuous increase in the volume of biomedical literature, the task of automatic biomedical text summarization has become increasingly important. In 2021 alone, 1,767,637 articles were published in the PubMed data...
Multiple Prior Knowledge Graph Neural Network in Multi-Omics Data Analysis Background Introduction Precision medicine is an important field for the future of healthcare as it provides personalized treatment plans for patients, improving treatment outcomes and reducing costs. For instance, due to the complex clinical, pathological, and molecular cha...
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...
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: 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...
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)...
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...
Incidence of Hormone-Related Cancers and Intimate Care Products Introduction In recent years, there has been an increasing concern about the safety of intimate care products that may contain potential endocrine disrupting chemicals, such as phthalates, parabens, and bisphenols. These chemicals are believed to alter endogenous hormone levels, influe...
Multicenter Prospective Study: Feasibility of Electronic Patient-Reported Outcomes (ePROs) in Elderly Cancer Patients Research Background In recent years, telemedicine has developed rapidly, especially during the COVID-19 pandemic, and is considered a solution to the problem of medical personnel shortages. Electronic patient-reported outcomes (ePRO...