Search AMIA.org

Electronic Health Records (EHRs) sampled from different populations can introduce unwanted biases, limit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this context, our main goal is to design an effective method to transfer knowledge between population groups, with computable guarantees for suitability, and that can be applied to quantify treatment disparities.

Negative descriptors in electronic health records (EHR) contribute to worse health outcomes; studies show they are also more prevalent in EHRs of women and racial minorities and affect downstream research biases. Similar and unique patterns of negative descriptors may also exist in the records of blind patients, including those with diabetic retinopathy. Diabetic retinopathy is a preventable but leading cause of blindness in the US that is disproportionally high among [...]

Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy.

By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies.

Clinicians retrieve data from electronic health record (EHR) systems and summarize them into clinical information to accomplish clinical reasoning and decision-making tasks. Visualization, using meaningful summarization methods and intuitive presentation approaches, can enhance this process. This systematic review examines how EHR data are summarized, visualized, and aligned with the 7 clinical reasoning and decision-making tasks shared by clinicians.

To explore the potential effects of recent restrictions on discussions regarding diversity, equity, and inclusion (DEI) in the field of biomedical informatics.

Risk prediction models are used in hospitals to identify pediatric patients at risk of clinical deterioration, enabling timely interventions and rescue. The objective of this study was to develop a new explainer algorithm that uses a patient's clinical notes to generate text-based explanations for risk prediction alerts.

This review paper comprehensively summarizes healthcare provider (HCP) evaluation of explanations produced by explainable artificial intelligence methods to support point-of-care, patient-specific, clinical decision-making (CDM) within medical settings. It highlights the critical need to incorporate human-centered (HCP) evaluation approaches based on their CDM needs, processes, and goals.

Family data are a valuable data source in bioinformatic research. This is because family members often share common genetic and environmental exposures. Collecting this family data is traditionally very labor intensive but advances in electronic health record (EHR) data mining has proven useful when identifying pedigrees linked to longitudinal health histories. These are called e-pedigrees. Unfortunately, e-pedigrees tend to miss the oldest patients who inherently have the longest and richest [...]