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This study proposes a novel Prior knowledge guided Integrated likelihood Estimation (PIE) method to correct bias in estimations of associations due to misclassification of electronic health record (EHR)-derived binary phenotypes, and evaluates the performance of the proposed method by comparing it to 2 methods in common practice.

To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources.

To assess clinical documentation variations across health care institutions using different electronic medical record systems and investigate how they affect natural language processing (NLP) system portability.

Existing general clinical natural language processing (NLP) systems such as MetaMap and Clinical Text Analysis and Knowledge Extraction System have been successfully applied to information extraction from clinical text. However, end users often have to customize existing systems for their individual tasks, which can require substantial NLP skills. Here we present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP [...]

We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents.

Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users [...]

Geocoding and characterizing geographic, community, and environmental characteristics of study participants is frequently done in epidemiological studies. However, participant addresses are identifiable protected health information (PHI) and geocoding must be conducted in a Health Insurance Portability and Accountability Act-compliant manner. Our objective was to create a software application for this process that addresses limitations in current approaches.

We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.

Electronic health record phenotyping is the use of raw electronic health record data to assert characterizations about patients. Researchers have been doing it since the beginning of biomedical informatics, under different names. Phenotyping will benefit from an increasing focus on fidelity, both in the sense of increasing richness, such as measured levels, degree or severity, timing, probability, or conceptual relationships, and in the sense of reducing bias. Research agendas should [...]

Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships by probing the relationship between global exposure variance and disease risk variance by birth season.