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Transgender patients have particular needs with respect to demographic information and health records; specifically, transgender patients may have a chosen name and gender identity that differs from their current legally designated name and sex. Additionally, sex-specific health information, for example, a man with a cervix or a woman with a prostate, requires special attention in electronic health record (EHR) systems. The World Professional Association for Transgender Health (WPATH) is an [...]

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC).

Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier.

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI).

Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced [...]

DNA methylation, a regulator of gene expression, plays an important role in diverse biological processes including developmental process, carcinogenesis and aging. In particular, aberrant DNA methylation has been largely observed in several types of cancers. Currently, it is important to extract disease-specific gene sets associated with the regulation of DNA methylation.

Biomarkers for subtyping triple negative breast cancer (TNBC) are needed given the absence of responsive therapy and relatively poor prediction of survival. Morphology of cancer tissues is widely used in clinical practice for stratifying cancer patients, while genomic data are highly effective to classify cancer patients into subgroups. Thus integration of both morphological and genomic data is a promising approach in discovering new biomarkers for cancer outcome prediction. Here we [...]

To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs).

To identify the temporal relations between clinical events and temporal expressions in clinical reports, as defined in the i2b2/VA 2012 challenge.

Biomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method.