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Adverse drug events (ADEs) are undesired harmful effects resulting from use of a medication, and occur in 30% of hospitalized patients. The authors have developed a data-mining method for systematic, automated detection of ADEs from electronic medical records.

To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development.

To describe the perspectives of Regenstrief LOINC Mapping Assistant (RELMA) users before and after the deployment of Community Mapping features, characterize the usage of these new features, and analyze the quality of mappings submitted to the community mapping repository.

Semantic role labeling (SRL), which extracts a shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding natural language. Few studies in SRL have been conducted in the medical domain, primarily due to lack of annotated clinical SRL corpora, which are time-consuming and costly to build. The goal of this study is to investigate domain adaptation techniques for clinical SRL leveraging resources built [...]

Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.

We have identified 5 use cases that comprise a useful definition of an "open or interoperable electronic health record (EHR)." Each of these use cases represents important functionality that should be available to 1) clinicians, so they can provide safe and effective health care; 2) researchers, so they can advance our understanding of disease and health care processes; 3) administrators, so they can reduce their reliance on a single-source EHR [...]

To create a multilingual gold-standard corpus for biomedical concept recognition.

The Health Insurance Portability and Accountability Act Privacy Rule enables healthcare organizations to share de-identified data via two routes. They can either 1) show re-identification risk is small (e.g., via a formal model, such as k-anonymity) with respect to an anticipated recipient or 2) apply a rule-based policy (i.e., Safe Harbor) that enumerates attributes to be altered (e.g., dates to years). The latter is often invoked because it is interpretable [...]

Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability.