Tara Borlawsky, MA - Research-IQ

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DEPARTMENT OF BIOMEDICAL INFORMATICS

Research-IQ:

Development and Evaluation of an

Ontology-anchored Integrative Query Tool

Tara B. Borlawsky, MA

The Ohio State University, Department of Biomedical InformaticsThe Ohio State University, Center for Clinical and Translational Science


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Introduction

Increasing adoption ofintegrative and systems-level approaches to research and clinical care

Ability to manage and reason upon complex andlarge-scale data sets is of particular importance

EHRs, data warehouses and CTMSs generate vast volumes ofpotentially high-quality individual and population-level phenotype data

LISs and other high-throughput instrumentation provide access tomulti-dimensional bio-molecular measurements

To empower non-technical domain experts with theability to reason upon and pose questions related to such

heterogeneous data sets, we have developed the

Research Integrative Query (Research-IQ) platform.


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BACKGROUND


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Knowledge-anchored Query Tools


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METHODS AND RESULTS


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Study Design


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Phase 1:Knowledge Engineering

1. Knowledge engineer modeled contents of the OAI datadictionary as UML class diagram

2. MetaMap used to annotate variable categories and casereport form questions with SNOMED-CT concepts

3. Semantically annotated data dictionary was representedin Apelon Distributed Terminology System (DTS)


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Phase 2:Prototype System Design


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Phase 3:Efficacy Evaluation

Q1 Does above average height or weight correlate with increased knee swelling or tendinitis?

Q2 Do changes in body mass index affect severity of knee pain?

Q3 Does smoking or alcohol use affect knee pain or stiffness?

Q4 Does a family history of osteoarthritis correlate with increased hip pain?

Q5 If patient knee pain limits activity, does that lead to a higher risk of depression?


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Phase 3:Efficacy Evaluation


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Phase 3:Efficacy and Usability Evaluation

Survey Question Average

Overall Reactions to Software

Terrible (0) Wonderful (9) 6.75

Difficult (0) Easy (9) 7.25

Frustrating (0) Satisfying (9) 5.00

Functionality and Interface

Display of information throughout the software was:

Confusing (0) Very Clear (9)7.50

Results generated by software were:Inconsistent (0) Consistent (9)

7.50

Results generated by the software were able to

satisfy your intent when interacting with the software:

Never (0) Always (9)6.75


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DISCUSSION


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Limitations

Most SNOMED-CT concepts associated with input researchquestions only partially represented the key conceptual concepts Additional tuning of the conceptual knowledge annotation workflow included in

Research-IQ may significantly improve its performance

SMEs were inconsistent with their assessments of the relevancy ofthe returned OAI data dictionary elements with respect to input

research questions

Majority of the retrieved variables were still considered to be relevant

Lengthy response time associated with conducting queries using theApelon DTS API Limited number of available SMEs


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Future Work

Web-based, grid-enabled, cohort discovery and expertiseresources

Optimized and streamlined metadata generation workflow Annotation accuracy evaluation Solutions for storing, querying and reasoning over the

semantically annotated models

Alternate open-source text annotation frameworks and algorithms Integration of a standards-based metadata repository

Expansion to more generalizable knowledge resource portal for the clinicaland translational research community that will provide forresource

discovery, explanation and utilization across a variety of resources.


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Take Home Points

Feasible to utilize well-validated conceptual knowledge

engineering and information retrieval techniques to develop a

highly usable and intuitive interface for non-technical domain

experts to develop and execute complex queries

ResearchIQ has the potential to increase the accessibility and

adoption of query and analysis tools targeting integrative, multi-

modal research data sets as are found in the contemporaryclinical and translational research domain.


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Acknowledgements

Co-authors Omkar Lele, MS Philip R. O. Payne, PhD

Contributions made by Peter Embi, MD, MS Rebecca Jackson, MD Thomas Best, MD

This work was supported by NIH/NCRR Grant Number UL1-RR025755


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Multi-dimensional Biomedical Data

Characterization of human subjects or populations usingexisting or relatively low-cost data sources1

Systematic evaluation can inform modeling of etiology or course ofnormal and disease states

Challenges associated with optimally taking advantage of the clinicalenvironment for this purpose

Ongoing research to overcome these barriers: Definition and widespread adoption of robust, scalable and sufficiently

expressive research-centric data modeling and exchange standards4

Mitigation of obstacles that may impede or prevent the secondary-useof primarily operational or clinical data for research purposes4-6

Development and validation of integrative and easily configurable datacollection, exchange, query and dissemination platforms7


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Phase 1:Knowledge Engineering

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Tara Borlawsky, MA - Research-IQ

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