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

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 [...]

Real-world data (RWD) analyses primarily rely on structured clinical documentation collected through routine clinical care or driven by medical billing requirements. Patient-reported outcome measures (PROMs), integrated into electronic health records (EHRs), are an additional data source that could offer valuable insights into a patient's perspective and contribute to a more comprehensive understanding of health outcomes in RWD studies. This study aims to characterize agreement between PROMs symptoms and structured clinical [...]

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.

In real-world data (RWD), defining the observation period-the time during which a patient is considered observable-is critical for estimating incidence rates (IRs) and other outcomes. Yet, in the absence of explicit enrollment information, this period must often be inferred, introducing potential bias.

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.

We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges.

Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention.

To improve prediction of chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) using machine learning (ML) and deep learning (DL) models applied to integrated clinical and claims data with varying observation windows, supported by explainable artificial intelligence (AI) to enhance interpretability and reduce bias.