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The purpose of this study was to understand the ethical, legal, and social issues described by parents of children with known or suspected genetic conditions that cause intellectual and developmental disabilities regarding research use of their child's electronic health record (EHR).

Development of systematic approaches for understanding and assessing data quality is becoming increasingly important as the volume and utilization of health data steadily increases. In this study, a taxonomy of data defects was developed and utilized when automatically detecting defects to assess Medicaid data quality maintained by one of the states in the United States.

We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.

Survival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of [...]

To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the [...]

To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.

Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.

The study sought to assess, for children in one large health system, (1) characteristics of active users of the patient portal (≥1 use in prior 12 months), (2) portal use by adolescents, and (3) variations in pediatric patient portal use.

This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.