Relational machine learning for electronic health record-driven phenotyping

OBJECTIVE

Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping.

METHODS

Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance.

RESULTS

We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p=0.039), J48 (p=0.003) and JRIP (p=0.003).

DISCUSSION

ILP has the potential to improve phenotyping by independently delivering clinically expert interpretable rules for phenotype definitions, or intuitive phenotypes to assist experts.

CONCLUSION

Relational learning using ILP offers a viable approach to EHR-driven phenotyping.

Enhancing the power of genetic association studies through the use of silver standard cases derived from electronic medical records

The feasibility of using imperfectly phenotyped "silver standard" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.

Medical providers' dental information needs: a baseline survey

Articulation of medical and dental practices has been strongly called for based on the many oral-systemic connections. With the rapid development and adoption of electronic health records, the feasibility of integrating medical and dental patient data should be strongly considered. The objective of this study was to develop an initial understanding of the medical providers' core dental information needs and opinion of integrated medical-dental electronic health record (iEHR) environment in their workflow. This was achieved by administering a 13 question survey to a group of 1,197 medical care providers employed by Marshfield Clinic in Wisconsin, United States. The survey received a response rate of 35%. The responses were analyzed based on provider 'Role' and 'Specialty'. The majority of the respondents felt the need for patient's dental information to coordinate or provide effective medical care. An integrated electronic health record environment could facilitate this holistic patient care approach.