Richard C. Kiefer

Desiderata for computable representations of electronic health records-driven phenotype algorithms.

Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank

BackgroundThe ability to conduct genome-wide association studies (GWAS) has enabled new exploration of how genetic variations contribute to health and disease etiology. However, historically GWAS have been limited by inadequate sample size due to associated costs for genotyping and phenotyping of study subjects. This has prompted several academic medical centers to form “biobanks” where biospecimens linked to personal health information, typically in electronic health records (EHRs), are collected and stored on a large number of subjects. This provides tremendous opportunities to discover novel genotype-phenotype associations and foster hypotheses generation.ResultsIn this work, we study how emerging Semantic Web technologies can be applied in conjunction with clinical and genotype data stored at the Mayo Clinic Biobank to mine the phenotype data for genetic associations. In particular, we demonstrate the role of using Resource Description Framework (RDF) for representing EHR diagnoses and procedure data, and enable federated querying via standardized Web protocols to identify subjects genotyped for Type 2 Diabetes and Hypothyroidism to discover gene-disease associations. Our study highlights the potential of Web-scale data federation techniques to execute complex queries.ConclusionsThis study demonstrates how Semantic Web technologies can be applied in conjunction with clinical data stored in EHRs to accurately identify subjects with specific diseases and phenotypes, and identify genotype-phenotype associations.

Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research

OBJECTIVE To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development. MATERIALS AND METHODS Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. RESULTS Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. DISCUSSION Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. CONCLUSIONS Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.

Using linked data for mining drug-drug interactions in electronic health records

By nature, healthcare data is highly complex and voluminous. While on one hand, it provides unprecedented opportunities to identify hidden and unknown relationships between patients and treatment outcomes, or drugs and allergic reactions for given individuals, representing and querying large network datasets poses significant technical challenges. In this research, we study the use of Semantic Web and Linked Data technologies for identifying drug-drug interaction (DDI) information from publicly available resources, and determining if such interactions were observed using real patient data. Specifically, we apply Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic as Resource Description Framework (RDF), and identify potential drug-drug interactions (PDDIs) for widely prescribed cardiovascular and gastroenterology drugs. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study patient health outcomes as well as enabling genome-guided drug therapies and treatment interventions.

A Standards-based Semantic Metadata Repository to Support EHR-driven Phenotype Authoring and Execution

This study describes our efforts in developing a standards-based semantic metadata repository for supporting electronic health record (EHR)-driven phenotype authoring and execution. Our system comprises three layers: 1) a semantic data element repository layer; 2) a semantic services layer; and 3) a phenotype application layer. In a prototype implementation, we developed the repository and services through integrating the data elements from both Quality Data Model (QDM) and HL7 Fast Healthcare Inteoroperability Resources (FHIR) models. We discuss the modeling challenges and the potential of our system to support EHR phenotype authoring and execution applications.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

Mining Anti-coagulant Drug-Drug Interactions from Electronic Health Records Using Linked Data

By nature, healthcare data is highly complex and voluminous. While on one hand, it provides unprecedented opportunities to identify hidden and unknown relationships between patients and treatment outcomes, or drugs and allergic reactions for given individuals, representing and querying large network datasets poses significant technical challenges. In this research, we study the use of Semantic Web and Linked Data technologies for identifying potential drug-drug interaction (PDDI) information from publicly available resources, and determining if such interactions were observed using real patient data. Specifically, we apply Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic as Resource Description Framework (RDF), and identify PDDIs for widely prescribed anti-coagulant Warfarin. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study prescription trends based on gender and age as well as patient health outcomes.

Mining drug-drug interaction patterns from linked data: A case study for Warfarin, Clopidogrel, and Simvastatin

By nature, healthcare data is highly complex and voluminous. While on one hand, it provides unprecedented opportunities to identify hidden and unknown relationships between patients and treatment outcomes, or drugs and allergic reactions for given individuals, representing and querying large network datasets poses significant technical challenges. In this research, we study the use of Semantic Web and Linked Data technologies for identifying potential drug-drug interaction (DDI) information from publicly available resources, and determining if such interactions were observed using real patient data. Specifically, we apply Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic as Resource Description Framework (RDF) graphs, and identify potential DDIs for three widely prescribed cardiovascular drugs: Warfarin, Clopidogrel and Simvastatin. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study patient health outcomes as well as enabling genome-guided drug therapies and treatment interventions.

The linked clinical data project: applying semantic web technologies for clinical and translational research using electronic medical records

Systematic study of clinical phenotypes is important to better understand the genetic basis of human diseases and more effective gene-based disease management. The Linked Clinical Data (LCD) project at Mayo Clinic aims to develop a semantics-driven framework for high-throughput phenotype extraction, representation, integration, and querying from electronic medical records using emerging Semantic Web technologies, such as Linked Open Data. This poster abstract provides a brief background and overview of the recently initiated LCD project.

Validation and discovery of genotype-phenotype associations in chronic diseases using linked data.

This study investigates federated SPARQL queries over Linked Open Data (LOD) in the Semantic Web to validate existing, and potentially discover new genotype-phenotype associations from public datasets. In particular, we report our preliminary findings for identifying such associations for commonly occurring chronic diseases using the Online Mendelian Inheritance in Man (OMIM) and Database for SNPs (dbSNP) within the LOD knowledgebase and compare them with Gene Wiki for coverage and completeness. Our results indicate that Semantic Web technologies can play an important role for in-silico identification of novel disease-gene-SNP associations, although additional verification is required before such information can be applied and used effectively.