Many medications have potentially serious adverse effects detected only after FDA approval. After 80 million people worldwide received prescriptions for the drug rofecoxib (Vioxx), its manufacturer withdrew it from the marketplace in 2004. Epidemiological data showed that it increases risk of heart attack and stroke. Recently, the FDA warned that the commonly prescribed statin drug class (e.g., Lipitor, Zocor, Crestor) may increase risk of memory loss and Type 2 diabetes. These incidents illustrate the difficulty of identifying adverse effects of prescription medications during premarketing trials. Only post-marketing surveillance can detect some types of adverse effects (e.g., those requiring years of exposure). We explored the use of data mining on clinical notes to detect novel adverse drug effects. We constructed a knowledge base using UMLS and other data sources that could classify drug-finding pairs as currently known adverse effects (drug causes finding), known indications (drug treats/prevents finding), or unknown relationship. We used natural language processing (NLP) to extract current medications and clinical findings (including diseases) from 360,000 de-identified history and physical examination (H&P) notes. We identified 35,000 interesting co-occurrences of medication-finding concepts that exceeded threshold probabilities of appearance. These involved ~600 drugs and ~2000 findings. Among the identified pairs are several that the FDA recognized as harmful in postmarketing surveillance, including rofecoxib and heart attack, rofecoxib and stroke, statins and diabetes, and statins and memory loss. Our preliminary results illustrate both the problems and potential of using data mining of clinical notes for adverse drug effect discovery.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-07022012-120906 |
| Date | 05 July 2012 |
| Creators | Smith, Joshua Carl |
| Contributors | Randolph A. Miller, Joshua C. Denny, W. Anderson Spickard, III, S. Trent Rosenbloom |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-07022012-120906/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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