TY - GEN
T1 - Biomedical Informatics Investigator
AU - Elkin, Peter L.
AU - Mullin, Sarah
AU - Sakilay, Sylvester
N1 - Publisher Copyright:
© 2018 The authors and IOS Press.
PY - 2018
Y1 - 2018
N2 - The BMI Investigator is a computer human interface built in.Net which allows simultaneous query of structured data such as demographics, administrative codes, medications (coded in RxNorm), laboratory test results (coded in LOINC) and formerly unstructured data in clinical notes (coded in SNOMED CT). The ontology terms identified using SNOMED are all coded as either positive, negative or uncertain assertions. They are then where applicable built into compositional expressions and stored in both a graph database and a triple store. The SNOMED CT codes are stored in a NOSQL database, Berkley DB, and the structured data is stored in SQL using the OMOP/OHDSI format. The BMI investigator also lets you develop models for cohort selection (data driven recruitment to clinical trials) and automated retrospective research using genomic criteria and we are adding image feature data currently to the system. We performed a usability experiment and the users identified some usability flaws which were used to improve the software. Overall, the BMI Investigator was felt to be usable by subject matter experts. Next steps for the software are to integrate genomic criteria and image features into the query engine.
AB - The BMI Investigator is a computer human interface built in.Net which allows simultaneous query of structured data such as demographics, administrative codes, medications (coded in RxNorm), laboratory test results (coded in LOINC) and formerly unstructured data in clinical notes (coded in SNOMED CT). The ontology terms identified using SNOMED are all coded as either positive, negative or uncertain assertions. They are then where applicable built into compositional expressions and stored in both a graph database and a triple store. The SNOMED CT codes are stored in a NOSQL database, Berkley DB, and the structured data is stored in SQL using the OMOP/OHDSI format. The BMI investigator also lets you develop models for cohort selection (data driven recruitment to clinical trials) and automated retrospective research using genomic criteria and we are adding image feature data currently to the system. We performed a usability experiment and the users identified some usability flaws which were used to improve the software. Overall, the BMI Investigator was felt to be usable by subject matter experts. Next steps for the software are to integrate genomic criteria and image features into the query engine.
KW - Automated retrospective research
KW - clinical genomic trial recruitment
KW - Clinical Research Informatics
KW - Ontology
KW - Recruitment to clinical trials
UR - https://www.scopus.com/pages/publications/85054775180
U2 - 10.3233/978-1-61499-921-8-195
DO - 10.3233/978-1-61499-921-8-195
M3 - Conference contribution
C2 - 30306935
AN - SCOPUS:85054775180
SN - 9781614999201
T3 - Studies in Health Technology and Informatics
SP - 195
EP - 199
BT - Decision Support Systems and Education
A2 - Fister, Kristina
A2 - Hagglund, Maria
A2 - Sonicki, Zdenko
A2 - Kolokathi, Aikaterini
A2 - Hercigonja-Szekeres, Mira
A2 - Mantas, John
A2 - Crisan-Vida, Mihaela
PB - IOS Press
ER -