Abstract: Elkin: Difference between revisions
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We then compare B/A with D/C using for example a Pearson chi square test. (This is a real example: people with Rosacia had a two-fold rate of OSA.) | We then compare B/A with D/C using for example a Pearson chi square test. (This is a real example: people with Rosacia had a two-fold rate of OSA.) | ||
But how to get these numbers? Our strategy is to extract all relevant prior stored codes from the Electronic Health Records of large numbers of patients and to use ontologies to enhance the resultant data by associating common ontology terms with multiple codes whenever multiple codes refer to the same phenomenon on the side of the patient. We can then the power of the | But how to get these numbers? Our strategy is to extract all relevant prior stored codes from the Electronic Health Records of large numbers of patients and to use ontologies to enhance the resultant data by associating common ontology terms with multiple codes whenever multiple codes refer to the same phenomenon on the side of the patient. We can then the power of the subsumption hierarchy within the ontology to find the transitive reflexive closure on subsumption for any given term used in our query in order to maximize the degree that we obtain all relevant data from our dataset even where the included data are coded in multiple different ways. | ||
In this way the person asking clinical research questions does not need to know all the subtypes of, for example, cardiovascular disorder or anti-arrhythmic drugs in order to have access to the corresponding data. The normalization of the many ways to say the same thing provided by use of a single ontology term facilitates reliable information retrieval. Coupling these characteristics with an interface allows subject-matter experts with no computer science background to ask complex questions of clinical data and get answers within seconds. | |||
Revision as of 16:35, 26 June 2016
Ontology-Enabled IRB-Free 5-Minute Retrospective Clinical Trials
Let’s say we are interested in the following research question:
- Do patients who have Rosacia have a higher risk of Obstructive Sleep Apnea than the general population?
To answer this question we need to know:
- A. Number of Patients in total who do not have Rosacia
- B. Number of Patients who do not have Rosacia and have OSA
- C. Number of Patients with Rosacia
- D. Number of Patients with Rosacia and OSA
We then compare B/A with D/C using for example a Pearson chi square test. (This is a real example: people with Rosacia had a two-fold rate of OSA.)
But how to get these numbers? Our strategy is to extract all relevant prior stored codes from the Electronic Health Records of large numbers of patients and to use ontologies to enhance the resultant data by associating common ontology terms with multiple codes whenever multiple codes refer to the same phenomenon on the side of the patient. We can then the power of the subsumption hierarchy within the ontology to find the transitive reflexive closure on subsumption for any given term used in our query in order to maximize the degree that we obtain all relevant data from our dataset even where the included data are coded in multiple different ways. In this way the person asking clinical research questions does not need to know all the subtypes of, for example, cardiovascular disorder or anti-arrhythmic drugs in order to have access to the corresponding data. The normalization of the many ways to say the same thing provided by use of a single ontology term facilitates reliable information retrieval. Coupling these characteristics with an interface allows subject-matter experts with no computer science background to ask complex questions of clinical data and get answers within seconds.
