Patients with hospital-acquired acute kidney injury (AKI) are at risk for increased mortality and further medical complications. Evaluating these patients with a prediction tool easily implemented within an electronic health record (EHR) would identify high-risk patients prior to the development of AKI and could prevent iatrogenically induced episodes of AKI and improve clinical management.

Electronic Health Records (EHRs) provide a real-world patient cohort for clinical and genomic research. Phenotype identification using informatics algorithms has been shown to replicate known genetic associations found in clinical trials and observational cohorts. However, development of accurate phenotype identification methods can be challenging, requiring significant time and effort.

The need for formal representations of eligibility criteria for clinical trials - and for phenotyping more generally - has been recognized for some time. Indeed, the availability of a formal computable representation that adequately reflects the types of data and logic evidenced in trial designs is a prerequisite for the automatic identification of study-eligible patients from Electronic Health Records.

Identification of patients' drug exposure information is critical to drug-related research that is based on electronic medical records (EMRs). Drug information is often embedded in clinical narratives and drug regimens change frequently because of various reasons like intolerance or insurance issues, making accurate modeling challenging. Here, we developed an informatics framework to determine patient drug exposure histories from EMRs by combining natural language processing (NLP) and machine learning (ML) technologies.

There is increasing interest in using electronic health records (EHRs) to identify subjects for genomic association studies, due in part to the availability of large amounts of clinical data and the expected cost efficiencies of subject identification. We describe the construction and validation of an EHR-based algorithm to identify subjects with age-related cataracts.

Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems.

Much epidemiologic information resides in literature, which is not in a computable format. To extract information and build knowledge bases of epidemiologic studies, we developed a system to extract noun phrases about epidemiologic exposures and outcomes. The system consists of two components: a natural language processing (NLP) engine; a machine learning (ML) based classifier. Four ML algorithms were applied and compared over different feature sets. To evaluate the performance of the system, we manually constructed an annotated dataset.

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).