The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information.

Electronic Medical Records (EMRs) are valuable resources for clinical observational studies. Smoking status of a patient is one of the key factors for many diseases, but it is often embedded in narrative text. Natural language processing (NLP) systems have been developed for this specific task, such as the smoking status detection module in the clinical Text Analysis and Knowledge Extraction System (cTAKES). This study examined transportability of the smoking module in cTAKES on the Vanderbilt University Hospital's EMR data.

The development of Electronic Health Record (EHR)-based phenotype selection algorithms is a non-trivial and highly iterative process involving domain experts and informaticians. To make it easier to port algorithms across institutions, it is desirable to represent them using an unambiguous formal specification language. For this purpose we evaluated the recently developed National Quality Forum (NQF) information model designed for EHR-based quality measures: the Quality Data Model (QDM).

Clinical Natural Language Processing (NLP) systems extract clinical information from narrative clinical texts in many settings. Previous research mentions the challenges of handling abbreviations in clinical texts, but provides little insight into how well current NLP systems correctly recognize and interpret abbreviations. In this paper, we compared performance of three existing clinical NLP systems in handling abbreviations: MetaMap, MedLEE, and cTAKES.

Health information technologies facilitate the collection of massive quantities of patient-level data. A growing body of research demonstrates that such information can support novel, large-scale biomedical investigations at a fraction of the cost of traditional prospective studies. While healthcare organizations are being encouraged to share these data in a de-identified form, there is hesitation over concerns that it will allow corresponding patients to be re-identified.

To develop a comprehensive temporal information extraction system that can identify events, temporal expressions, and their temporal relations in clinical text. This project was part of the 2012 i2b2 clinical natural language processing (NLP) challenge on temporal information extraction.

The ADME Core Panel assays 184 variants across 34 pharmacogenes, many of which are difficult to accurately genotype with standard multiplexing methods.

Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.

Worldwide adoption of Electronic Medical Records (EMRs) databases in health care have generated an unprecedented amount of clinical data available electronically. There has been an increasing trend in US and western institutions towards collaborating with China on medical research using EMR data. However, few studies have investigated characteristics of EMR data in China and their differences with the data in US hospitals.