Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms.

Mayo Clinic, Rochester, MN, USA.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2011; 2011:274-83.
Source: PubMed

ABSTRACT 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. As part of the wider process of representation development, this paper reports on an analysis of fourteen Electronic Health Record oriented phenotyping algorithms (developed as part of the eMERGE project) in terms of their constituent data elements, types of logic used and temporal characteristics. We discovered that the majority of eMERGE algorithms analyzed include complex, nested boolean logic and negation, with several dependent on cardinality constraints and complex temporal logic. Insights gained from the study will be used to augment the CDISC Protocol Representation Model.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Narrative resources in electronic health records make clinical phenotyping study difficult to achieve. If a narrative patient history can be represented in a timeline, this would greatly enhance the efficiency of information-based studies. However, current timeline representations have limitations in visualizing narrative events. In this paper, we propose a temporal model named the 'V-Model' which visualizes clinical narratives into a timeline.
    BMC Medical Informatics and Decision Making 01/2014; 14(1):90. DOI:10.1186/1472-6947-14-90 · 1.50 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment. Materials and methods We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute. Results Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70-0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy. Conclusions It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.
    Journal of the American Medical Informatics Association 07/2014; 21(6). DOI:10.1136/amiajnl-2014-002733 · 3.93 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics.
    Yearbook of medical informatics 01/2014; 9(1):215-23. DOI:10.15265/IY-2014-0009

Full-text (2 Sources)

Available from
May 16, 2014