Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model

Abstract

Background Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome. Method Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard. Results Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85–0.88 v. 0.54–0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001). Conclusions The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

Phenome-wide Heritability Analysis of the UK Biobank

“…However, our head-to-head comparison of the heritability estimates between self-reported illness and ICD-10 codes showed largely consistent results, indicating that both phenotypic approaches at least captured comparable variations in these phenotypes. Prior research evaluating phenotypes derived from electronic health records (EHR) indicate that greater phenotypic validity can be achieved when diagnostic codes are supplemented with text mining methods [55][56][57][58]. The specificity of the disease codes might also be improved by leveraging the medication records in the UK Biobank.…”

Efficient Genome-wide Association in Biobanks Using Topic Modeling Identifies Multiple Novel Disease Loci

“…Specifically, we apply latent Dirichilet allocation (LDA), a means of identifying commonly cooccurring features, to derive a set of 50 disease topics. Then we test those topics for association with common genetic variation and compare this approach to capture a given diagnosis varies widely, even when diagnosis-specific classifiers are applied to augment single codes (2,3). As such, approaches that focus on individual diagnostic codes are limited by inaccurate, missing or heterogeneous diagnoses; eg, where individuals with cystic fibrosis might be represented by male infertility, diabetes and chronic rhinosinusitis even in the absence of a diagnostic code for cystic fibrosis (4).…”

The revolution of personalized psychiatry: will technology make it happen sooner?

“…Promising proof exists that processing techniques can be effectively used to extract and code relevant information from unstructured data, e.g., applying natural language analytic tools to clinical notes (Perlis et al 2012;Castro et al 2015;McCoy et al 2015;Patel et al 2015b). For example, in a study by Perlis et al, the application of natural language processing technique to information provided by EMR clinical notes allowed to classify current mood state of inpatients with a billing diagnosis of major depressive disorder and define their longitudinal outcomes (Perlis et al 2012). Castro et al used similar techniques to design a diagnostic algorithm for Bipolar Disorder using information included in the EMR of patients of the Partners Healthcare Research Patient Data Registry .…”