A data-driven approach to automatic discovery of prescription drugs in cardiovascular risk management. Samadian, S., Good, B., M., McManus, B., & Wilkinson, M., D. In Proceedings of The BioOntologies SIG, ISMB 2012, pages 14-17, 2012.
A data-driven approach to automatic discovery of prescription drugs in cardiovascular risk management [link]Website  abstract   bibtex   
Objectives: To evaluate a data-driven approach for automatically identifying medications used in the treatment of cardiovascular disease, and consider how these learned rules might be applied to ontology curation, and evaluation. Methods: We mined the clinical records of a large cardiovascular patient cohort, focusing on their clinical phenotype and their prescribed medications. Machine learning algorithms from WEKA detected rules linking medications to patient’s treatment-status. These rules were then compared to axioms encoded in the NDF-RT Ontology. Results: For most medications in the dataset we were able to re-discover, with high precision, the prescriptive rules present in the NDF-RT; however, we discovered only 4/19 possible rules for medications linked to Chronic Heart Failure, and no rules for medications linked to Hypertension. We also show that, in some cases, these rules contain more detailed information than is present in the NDF-RT itself. Conclusion: This experiment demonstrates how data-driven approaches might be used to ameliorate the knowledge acquisition problem for ontology design. We show that the learned rules could be used to evaluate and improve an existing ontology (NDF-RT). We propose that these rules could be used to automatically construct ontological axioms, thus semi-automating the process of de novo ontology construction for a given domain.
@inproceedings{
 title = {A data-driven approach to automatic discovery of prescription drugs in cardiovascular risk management},
 type = {inproceedings},
 year = {2012},
 pages = {14-17},
 websites = {http://goo.gl/JAnh2},
 id = {e07bde1d-b1ea-3e5b-9dfa-b9455adbd312},
 created = {2014-07-02T09:11:39.000Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2017-03-22T07:45:59.566Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Samadian2012},
 private_publication = {false},
 abstract = {Objectives: To evaluate a data-driven approach for automatically identifying medications used in the treatment of cardiovascular disease, and consider how these learned rules might be applied to ontology curation, and evaluation. Methods: We mined the clinical records of a large cardiovascular patient cohort, focusing on their clinical phenotype and their prescribed medications. Machine learning algorithms from WEKA detected rules linking medications to patient’s treatment-status. These rules were then compared to axioms encoded in the NDF-RT Ontology. Results: For most medications in the dataset we were able to re-discover, with high precision, the prescriptive rules present in the NDF-RT; however, we discovered only 4/19 possible rules for medications linked to Chronic Heart Failure, and no rules for medications linked to Hypertension. We also show that, in some cases, these rules contain more detailed information than is present in the NDF-RT itself. Conclusion: This experiment demonstrates how data-driven approaches might be used to ameliorate the knowledge acquisition problem for ontology design. We show that the learned rules could be used to evaluate and improve an existing ontology (NDF-RT). We propose that these rules could be used to automatically construct ontological axioms, thus semi-automating the process of de novo ontology construction for a given domain.},
 bibtype = {inproceedings},
 author = {Samadian, Soroush and Good, Benjamin M. and McManus, Bruce and Wilkinson, Mark D.},
 booktitle = {Proceedings of The BioOntologies SIG, ISMB 2012}
}
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