Characterizing Physicians Practice Phenotype from Unstructured Electronic Health Records.

Clinical practice varies among physicians in ways that could lead to variation in what is documented in a patient's electronic health records (EHR) and act as a source of bias to predictive model performance that is independent of patient health status. We used EHR encounter note data on 5,187primary care patients 50 to 85 years of age selected for a separate case-control study covering 144 unique primary care physicians (PCPs). A validated text extractor tool was used to identify mentions of Framingham heartfailure signs and symptoms (FHFSS) from the notes.

Early detection of heart failure with varying prediction windows by structured and unstructured data in electronic health records.

Heart failure (HF) prevalence is increasing and is among the most costly diseases to society. Early detection of HF would provide the means to test lifestyle and pharmacologic interventions that may slow disease progression and improve patient outcomes. This study used structured and unstructured data from electronic health records (EHR) to predict onset of HF with a particular focus on how prediction accuracy varied in relation to time before diagnosis.

Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.

Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR. PredMED achieved an 84.4% F-score on office visit encounter notes and 95.

Prevalence of heart failure signs and symptoms in a large primary care population identified through the use of text and data mining of the electronic health record.

Background: The electronic health record (EHR) contains a tremendous amount of data that if appropriately detected can lead to earlier identification of disease states such as heart failure (HF). Using a novel text and data analytic tool we explored the longitudinal EHR of over 50,000 primary care patients to identify the documentation of the signs and symptoms of HF in the years preceding its diagnosis.

Methods And Results: Retrospective analysis consisted of 4,644 incident HF cases and 45,981 group-matched control subjects.

Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.

Objective: Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF.