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Development and evaluation of a machine learning algorithm to map medical conditions and procedures from real-world data
Descriptive
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Title
Development and evaluation of a machine learning algorithm to map medical conditions and procedures from real-world data
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Makadia
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Rupa
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1984-
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Rupa Makadia
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author
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Srinivasan
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Shankar
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Shankar Srinivasan
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Advisory Committee
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Rutgers University
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School of Health Professions
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school
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theses
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2019
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2019-05
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2019
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English
Abstract
Background: Ontologies characterize complex and detailed information and are extensively used in healthcare research. Medical information (textbooks, expert opinions, clinical evidence) has information on conditions and its corresponding procedures (treatments), but this information is not captured or structured in any ontology. The objective of the research is to create a condition-procedure ontology from real world data to be utilized in observational research or electronic health record (EHR) system.
Methods: Predictive models are developed to learn from five datasets (administrative claims, hospital charge data) to generate two algorithms (diagnostic and therapeutic) to predict condition-procedure relationships in the SNOMED-CT vocabulary. A reference set with 100 positive pairs per algorithm, and 32,132 negative pairs were developed. Predictive models were constructed by designing 51 possible covariates that describe condition-procedure pairs from Optum© De-Identified Clinformatics® Data Mart Database – Socio-Economic Status (Optum) dataset and determining which covariates discriminated between the positive and negative controls, as measured by Area Under Receiver Operator Characteristic Curve (AUC). External validation of the final algorithms was performed on 4 other databases. The final algorithms were applied across the universe of condition and procedure pairs in all five databases to construct the full condition-procedure ontology, and the ontology was evaluated for validity and coverage of condition and procedure concepts from the set of identified condition-procedure pairs. An additional analysis was trained to classify diagnostic vs. therapeutic intervention based on the overlap of pairs within the two algorithms.
Results: Algorithms include the following covariates: condition-procedure occurring together, relative risk, support and sensitivity. Both algorithms had AUCs greater than .90, and external validation also showed similar results. In Optum, 98% of conditions and 63% of procedure codes had at least one relationship identified in the ontology. The intervention type analysis resulted in an AUC of 0.79.
Conclusions: Real-world data can be utilized to construct a medical ontology of condition-procedure relationships with strong performance and good coverage. These results can be utilized to fuel research efforts in healthcare such as cohort generation and computer provider order entry systems by understanding conditions and procedures and their application to diagnose or treat a patient.
Methods: Predictive models are developed to learn from five datasets (administrative claims, hospital charge data) to generate two algorithms (diagnostic and therapeutic) to predict condition-procedure relationships in the SNOMED-CT vocabulary. A reference set with 100 positive pairs per algorithm, and 32,132 negative pairs were developed. Predictive models were constructed by designing 51 possible covariates that describe condition-procedure pairs from Optum© De-Identified Clinformatics® Data Mart Database – Socio-Economic Status (Optum) dataset and determining which covariates discriminated between the positive and negative controls, as measured by Area Under Receiver Operator Characteristic Curve (AUC). External validation of the final algorithms was performed on 4 other databases. The final algorithms were applied across the universe of condition and procedure pairs in all five databases to construct the full condition-procedure ontology, and the ontology was evaluated for validity and coverage of condition and procedure concepts from the set of identified condition-procedure pairs. An additional analysis was trained to classify diagnostic vs. therapeutic intervention based on the overlap of pairs within the two algorithms.
Results: Algorithms include the following covariates: condition-procedure occurring together, relative risk, support and sensitivity. Both algorithms had AUCs greater than .90, and external validation also showed similar results. In Optum, 98% of conditions and 63% of procedure codes had at least one relationship identified in the ontology. The intervention type analysis resulted in an AUC of 0.79.
Conclusions: Real-world data can be utilized to construct a medical ontology of condition-procedure relationships with strong performance and good coverage. These results can be utilized to fuel research efforts in healthcare such as cohort generation and computer provider order entry systems by understanding conditions and procedures and their application to diagnose or treat a patient.
Subject
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Topic
Machine learning
Subject
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Biomedical Informatics
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Knowledge management
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Rutgers University Electronic Theses and Dissertations
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1 online resource (187 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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School of Health Professions ETD Collection
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doi:10.7282/t3-ezab-zd23
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ETD doctoral
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The author owns the copyright to this work.
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Makadia
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Rupa
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2019-04-15 06:54:17
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Rupa Makadia
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Rutgers University. School of Health Professions
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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