Sarvnaz Karimi
Chen Wang
Skip Abstract Section
Skip Supplemental Material SectionAbstract
We review data mining and related computer science techniques that have been studied in the area of drug safety to identify signals of adverse drug reactions from different data sources, such as spontaneous reporting databases, electronic health records, and medical literature. Development of such techniques has become more crucial for public heath, especially with the growth of data repositories that include either reports of adverse drug reactions, which require fast processing for discovering signals of adverse reactions, or data sources that may contain such signals but require data or text mining techniques to discover them. In order to highlight the importance of contributions made by computer scientists in this area so far, we categorize and review the existing approaches, and most importantly, we identify areas where more research should be undertaken.
Supplemental Material
Available for Download
zip
karimi.zip (22.5 KB)
Supplemental movie, appendix, image and software files for, Text and Data Mining Techniques in Adverse Drug Reaction Detection
- Australian Commission on Safety and Quality in Health Care (ACSQHC). 2011. National Safety and Quality Health Service Standards. Sydney.Google Scholar
- A. Agresti. 2007. An Introduction to Categorical Data Analysis. Vol. 423. John Wiley & Sons.Google Scholar
- A. Alghabban. 2004. Dictionary of Pharmacovigilance. Pharmaceutical Press.Google Scholar
- N. Anderson and J. Borlak. 2011. Correlation versus causation? Pharmacovigilance of the analgesic flupirtine exemplifies the need for refined spontaneous ADR reporting. PloS One 6, 10 (2011), e25221.Google ScholarCross Ref
- A. R. Aronson. 2001. Effective mapping of biomedical text to the UMLS metathesaurus: The MetaMap program. In AMIA Annual Symposium Proceedings. 17--21.Google Scholar
- N. Atias and R. Sharan. 2011. An algorithmic framework for predicting side effects of drugs. Journal of Computational Biology 18, 3 (2011), 207--218.Google ScholarCross Ref
- P. Avillach, J.-C. Dufour, G. Diallo, F. Salvo, M. Joubert, F. Thiessard, F. Mougin, G. Trifirò, A. Fourrier-Réglat, and A. Pariente. 2013. Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: A contribution from the EU--ADR project. Journal of the American Medical Informatics Association 20, 3 (2013), 446--452.Google ScholarCross Ref
- J. Baggs, J. Gee, E. Lewis, G. Fowler, P. Benson, T. Lieu, A. Naleway, N. P. Klein, R. Baxter, and E. Belongia. 2011. The vaccine safety datalink: A model for monitoring immunization safety. Pediatrics 127, Supplement 1 (2011), S45--S53.Google ScholarCross Ref
- A. Bate, M. Lindquist, I. R. Edwards, S. Olsson, R. Orre, A. Lansner, and R. M. De Freitas. 1998. A Bayesian neural network method for adverse drug reaction signal generation. European Journal of Clinical Pharmacology 54, 4 (1998), 315--321.Google ScholarCross Ref
- A. Bate, M. Lindquist, I. R. Edwards, and R. Orre. 2002. A data mining approach for signal detection and analysis. Drug Safety 25, 6 (2002), 393--397.Google ScholarCross Ref
- D. W. Bates, R. S. Evans, H. Murff, P. D. Stetson, L. Pizziferri, and G. Hripcsak. 2003. Detecting adverse events using information technology. Journal of the American Medical Informatics Association 10, 2 (2003), 115--128.Google ScholarCross Ref
- A. Benton, L. Ungar, S. Hill, S. Hennessy, J. Mao, A. Chung, C. E. Leonard, and J. H. Holmes. 2011. Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of Biomedical Informatics 44, 6 (2011), 989--996. Google ScholarDigital Library
- J. Bian, U. Topaloglu, and F. Yu. 2012. Towards large-scale twitter mining for drug-related adverse events. In 2012 International Workshop on Smart Health and Wellbeing. 25--32. Google ScholarDigital Library
- C. Biriell and I. R. Edwards. 1997. Reasons for reporting adverse drug reactions -- some thoughts based on an international review. Pharmacoepidemiology and Drug Safety 6, 1 (1997), 21--26.Google ScholarCross Ref
- C. Bizer, T. Heath, and T. Berners-Lee. 2009. Linked data -- the story so far. International Journal on Semantic Web and Information Systems 5, 3 (2009), 1--22.Google ScholarCross Ref
- O. Bodenreider. 2004. The unified medical language system (UMLS): Integrating biomedical terminology. Nucleic Acids Research 32, Supplement 1 (2004), D267--D270.Google ScholarCross Ref
- O. Bodenreider. 2009. Using SNOMED CT in combination with MedDRA for reporting signal detection and adverse drug reactions reporting. In AMIA Annual Symposium Proceedings. 45--49.Google Scholar
- C. L. Burgess, C. D. Holman, and A. G. Satti. 2005. Adverse drug reactions in older australians, 1981--2002. Medical Journal of Australia 182, 6 (2005), 267--270.Google ScholarCross Ref
- J. Cable. 2009. Adverse events of statins - an informal internet-based study. Journal of Independent Medical Research 7, 1 (2009), 24 pages.Google Scholar
- S. Chaudhuri, V. Ganti, and R. Kaushik. 2006. A primitive operator for similarity joins in data cleaning. In 22nd International Conference on Data Engineering. 5--17. Google ScholarDigital Library
- B. W. Chee, R. Berlin, and B. Schatz. 2011. Predicting adverse drug events from personal health messages. In AMIA Annual Symposium Proceedings. 217--226.Google Scholar
- E. S. Chen, G. Hripcsak, H. Xu, M. Markatou, and C. Friedman. 2008. Automated acquisition of disease--drug knowledge from biomedical and clinical documents: An initial study. Journal of the American Medical Informatics Association 15, 1 (2008), 87--98.Google ScholarCross Ref
- J. Chen, H. He, J. Li, H. Jin, D. McAullay, G. Williams, R. Sparks, and C. Kelman. 2005. Representing association classification rules mined from health data. In 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems. 1225--1231. Google ScholarDigital Library
- J. Chen, H. Jin, H. He, D. Mcaullay, C. M. O’Keefe, R. Sparks, and C. Kelman. 2010. Mining consequence events in temporal health data. Intelligent Data Analysis - Knowledge Discovery in Bioinformatics 14, 2 (2010), 245--261. Google ScholarDigital Library
- P. Christen. 2008. Automatic record linkage using seeded nearest neighbour and support vector machine classification. In 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 151--159. Google ScholarDigital Library
- P. Christen. 2012. A survey of indexing techniques for scalable record linkage and deduplication. IEEE Transactions on Knowledge and Data Engineering 24, 9 (2012), 1537--1555. Google ScholarDigital Library
- CIOMS. 2010. Practical Aspects of Signal Detection in Pharmacovigilance. (2010).Google Scholar
- D. C. Classen, S. L. Pestotnik, R. S. Evans, and J. P. Burke. 1991. Computerized surveillance of adverse drug events in hospital patients. Journal of the American Medical Association 266, 20 (1991), 2847--2851.Google ScholarCross Ref
- D. C. Classen, S. L. Pestotnik, R. S. Evans, and J. P. Burke. 1992. Description of a computerized adverse drug event monitor using a hospital information system. Hospital Pharmacy 27, 9 (1992), 776--779.Google Scholar
- D. C. Classen, S. L. Pestotnik, R. S. Evans, J. F. Lloyd, and J. P. Burke. 1997. Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. JAMA 277, 4 (1997), 301--306.Google ScholarCross Ref
- N. Colineau and C. Paris. 2010. Talking about your health to strangers: Understanding the use of online social networks by patients. New Review of Hypermedia and Multimedia 16, 1--2 (2010), 141--160. Google ScholarDigital Library
- P. M. Coloma, M. J. Schuemie, G. Trifiró, R. Gini, R. Herings, J. Hippisley-Cox, G. Mazzaglia, C. Giaquinto, G. Corrao, L. Pedersen, J. van der Lei, and M. Sturkenboom. 2011. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiology Drug Safety 20, 1 (2011), 1--11.Google ScholarCross Ref
- P. M. Coloma, G. Trifiró, V. Patadia, and M. Sturkenboom. 2013. Postmarketing safety surveillance. Drug Safety 36, 3 (2013), 183--197.Google ScholarCross Ref
- T. M. Cover and P. E. Hart. 1967. Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 1 (1967), 21--27. Google ScholarDigital Library
- L. H. Curtis, M. G. Weiner, D. M. Boudreau, W. O. Cooper, G. W. Daniel, V. P. Nair, M. A. Raebel, N. U. Beaulieu, R. Rosofsky, and T. S. Woodworth. 2012. Design considerations, architecture, and use of the mini-sentinel distributed data system. Pharmacoepidemiology and Drug Safety 21, S1 (2012), 23--31.Google ScholarCross Ref
- C. M. Cusack. 2008. Electronic health records and electronic prescribing: Promise and pitfalls. Obstetrics and Gynecology Clinics of North America 35, 1 (2008), 63--79.Google ScholarCross Ref
- S. Doan, N. Collier, H. Xu, P. H. Duy, and T. M. Phuong. 2012. Recognition of medication information from discharge summaries using ensembles of classifiers. BMC Medical Informatics and Decision Making 12, 36 (2012), 10 pages.Google Scholar
- W. DuMouchel. 1999. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. American Statistician 53, 3 (1999), 177--190.Google Scholar
- W. DuMouchel and D. Pregibon. 2001. Empirical Bayes screening for multi-item associations. In 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 67--76. Google ScholarDigital Library
- I. R. Edwards and J. K. Aronson. 2000. Adverse drug reactions: Definitions, diagnosis, and management. The Lancet 356, 9237 (2000), 1255--1259.Google Scholar
- J. P. Ehsani, T. Jackson, and S. J. Duckett. 2006. The incidence and cost of adverse events in victorian hospitals 2003-04. Medical Journal of Australia 184, 11 (2006), 551--555.Google ScholarCross Ref
- M. G. Elfeky, V. S. Verykios, and A. K. Elmagarmid. 2002. TAILOR: A record linkage toolbox. In 18th International Conference on Data Engineering. 17--28. Google ScholarDigital Library
- EMA. 2012. The European Medicines Agency Guidelines on Good Pharmacovigilance Practices (GVP). Technical Report EMA/827661/2011. The European Medicines Agency.Google Scholar
- S. J. W. Evans, P. C. Waller, and S. Davis. 2001. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety 10, 6 (2001), 483--486.Google ScholarCross Ref
- FDA. 2014. FAERS Reporting by Healthcare Providers and Consumers by Year. Retrieved from http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm070456.htm.Google Scholar
- T. R. Fleming and D. P. Harrington. 1992. Counting processes and survival analysis. Statistics in Medicine 11, 13 (1992), 1792--1793.Google Scholar
- S. Fox and S. Jones. 2009. The Social Life Of Health Information. Technical Report. Pew Internet & American Life Project (2009).Google Scholar
- D. M. Fram, J. S. Almenoff, and W. DuMouchel. 2003. Empirical Bayesian data mining for discovering patterns in post-marketing drug safety. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 359--368. Google ScholarDigital Library
- C. Friedman. 2009. Discovering novel adverse drug events using natural language processing and mining of the electronic health record. In 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine. 1--5. Google ScholarDigital Library
- C. D. Furberg, A. A. Levin, P. A. Gross, R. S. Shapiro, and B. L. Strom. 2006. The FDA and drug safety: A proposal for sweeping changes. JAMA Internal Medicine 166, 18 (2006), 1938--1942.Google Scholar
- H. Gonzalez, A. Halevy, C. S. Jensen, A. Langen, J. Madhavan, R. Shapley, and W. Shen. 2010. Google fusion tables: Data management, integration and collaboration in the cloud. In 1st ACM Symposium on Cloud Computing. 175--180. Google ScholarDigital Library
- M. Gordon. 2008. Improving post-approval risk surveillance for drugs: Active post-market risk identification. Michigan Telecommunications and Technology Law Review 15, 297 (2008), 297--314.Google Scholar
- H. Gurulingappa, J. Fluck, M. Hofmann-Apitius, and L. Toldo. 2011. Identification of adverse drug event assertive sentences in medical case reports. In 1st International Workshop on Knowledge Discovery and Health Care Management. 16--27.Google Scholar
- H. Gurulingappa, A. M. Rajput, A. Roberts, J. Fluck, M. Hofmann-Apitius, and L. Toldo. 2012. Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of Biomedical Informatics 45, 5 (2012), 885--892. Google ScholarDigital Library
- A. Y. Halevy, A. Rajaraman, and J. J. Ordille. 2006. Data integration: The teenage years. In 32nd International Conference on Very Large Data Bases. Seoul, Korea, 9--16. Google ScholarDigital Library
- R. W. Hamming. 1950. Error detecting and error correcting codes. Bell System Technical Journal 29, 2 (1950), 147--160.Google ScholarCross Ref
- T. J. Hannan. 1999. Detecting adverse drug reactions to improve patient outcomes. International Journal of Medical Informatics 55, 1 (1999), 61--64.Google ScholarCross Ref
- R. Harpaz, S. Vilar, W. DuMouchel, H. Salmasian, K. Haerian, N. H. Shah, H. S. Chase, and C. Friedman. 2013. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. Journal of the American Medical Informatics Association 20, 3 (2013), 413--419.Google ScholarCross Ref
- C. Hawn. 2009. Take two aspirin and tweet me in the morning: How Twitter, Facebook, and other social media are reshaping health care. Health Affairs 28, 2 (2009), 361--368.Google ScholarCross Ref
- L. Hazell and S. A. Shakir. 2006. Under-reporting of adverse drug reactions: A systematic review. Drug Safety 29, 5 (2006), 385--396.Google ScholarCross Ref
- H. He, G. J. Williams, J. Chen, S. Hawkins, and C. Kelman. 2004. Exploring possible adverse drug reactions by clustering event sequences. In Data Warehousing and Knowledge Discovery. 199--208.Google Scholar
- A. B. Hill. 1965. The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine 58, 5 (1965), 295--300.Google Scholar
- B. Honigman, J. Lee, J. Rothschild, P. Light, R. M. Pulling, T. Yu, and D. W. Bates. 2001. Using computerized data to identify adverse drug events in outpatients. Journal of the American Medical Informatics Association 8, 3 (2001), 254--266.Google ScholarCross Ref
- B. L. Hug, C. Keohane, D. L. Seger, C. Yoon, and D. W. Bates. 2012. The costs of adverse drug events in community hospitals. Joint Commission Journal on Quality and Patient Safety 38, 3 (2012), 120--126.Google ScholarCross Ref
- R. K. Hulse, S. J. Clark, J. C. Jackson, H. R Warner, and R. M. Gardner. 1976. Computerized medication monitoring system. American Journal of Hospital Pharmacy 33, 10 (1976), 1061--1064.Google Scholar
- S. H. Hwang, S. Lee, H. K. Koo, and Y. Kim. 2008. Evaluation of a computer-based adverse-drug-event monitor. American Journal of Health-System Pharmacy 65, 23 (2008), 2265--2272.Google ScholarCross Ref
- ICH Working Group. 1996. ICH harmonised tripartite guideline: Guideline for good clinical practice E6 (R1). In International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use, Vol. 10.Google Scholar
- International Organization for Standardization (ISO). 2005. Health Informatics -- Electronic Health Record -- Definition, Scope and Context. Technical Report ISO/TR 20514.Google Scholar
- A. K. Jha, G. J. Kuperman, J. M. Teich, L. Leape, B. Shea, E. Rittenberg, E. Burdick, D. L. Seger, M. Vander Vliet, and D. W. Bates. 1998. Identifying adverse drug events: Development of a computer-based monitor and comparison with chart review and stimulated voluntary report. Journal of the American Medical Informatics Association 5, 3 (1998), 305--314.Google ScholarCross Ref
- Y. Ji, R. M. Massanari, J. Ager, J. Yen, R. E. Miller, and H. Ying. 2007. A fuzzy logic-based computational recognition-primed decision model. Information Sciences 177, 20 (2007), 4338--4353. Google ScholarDigital Library
- Y. Ji, H. Ying, P. Dews, A. Mansour, J. Tran, R. E. Miller, and R. M. Massanari. 2011. A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE Transactions on Information Technology in Biomedicine 15, 3 (2011), 428--437. Google ScholarDigital Library
- Y. Ji, H. Ying, J. Tran, P. Dews, A. Mansour, and R. M. Massanari. 2013. A method for mining infrequent causal associations and its application in finding adverse drug reaction signal pairs. IEEE Transactions on Knowledge and Data Engineering 25, 4 (2013), 721--733. Google ScholarDigital Library
- H. Jin, J. Chen, H. He, C. Kelman, D. McAullay, and C. M. O’Keefe. 2010. Signaling potential adverse drug reactions from administrative health databases. IEEE Transactions on Knowledge and Data Engineering 22, 6 (2010), 839--853. Google ScholarDigital Library
- H. Jin, J. Chen, H. He, G. J. Williams, C. Kelman, and C. M. O’Keefe. 2008. Mining unexpected temporal associations: Applications in detecting adverse drug reactions. IEEE Transactions on Information Technology in Biomedicine 12, 4 (2008), 488--500. Google ScholarDigital Library
- H. Jin, J. Chen, C. Kelman, H. He, D. McAullay, and C. M. O’Keefe. 2006. Mining unexpected associations for signalling potential adverse drug reactions from administrative health databases. In 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 867--876. Google ScholarDigital Library
- S. Karimi, S. N. Kim, and L. Cavedon. 2011. Drug side-effects: What do patient forums reveal? In 2nd International Workshop On Web Science and Information Exchange in the Medical Web. 14--15.Google Scholar
- M. Kay. 1986. Algorithm schemata and data structures in syntactic processing. In Readings in Natural Language Processing. Morgan Kaufmann Publishers Inc., San Francisco, CA. 35--70. Google ScholarDigital Library
- C. W. Kelman, S. Pearson, R. O. Day, C. D. J. Holman, E. V. Kliewer, and D. A. Henry. 2007. Evaluating medicines: Let’s use all the evidence. Medical Journal of Australia 186, 5 (2007), 249--252.Google ScholarCross Ref
- D. M. Kirking, J. W. Thomas, F. J. Ascione, and E. L. Boyd. 1986. Detecting and preventing adverse drug interactions: The potential contribution of computers in pharmacies. Social Science and Medicine 22, 1 (1986), 1--8.Google ScholarCross Ref
- J. Koch-Weser, E. M. Sellers, and R. Zacest. 1977. The ambiguity of adverse drug reactions. European Journal of Clinical Pharmacology 11, 2 (1977), 75--78.Google ScholarCross Ref
- M. Kuhn, M. Campillos, I. Letunic, L. J. Jensen, and P. Bork. 2010. A side effect resource to capture phenotypic effects of drugs. Molecular Systems Biology 6, 1 (2010), Article 343.Google Scholar
- R. Leaman, L. Wojtulewicz, R. Sullivan, A. Skariah, J. Yang, and G. Gonzalez. 2010. Towards internet-age pharmacovigilance: Extracting adverse drug reactions from user posts to health-related social networks. In Proceedings of the Workshop on Biomedical Natural Language Processing. Uppsala, Sweden, 117--125. Google ScholarDigital Library
- M. Lenzerini. 2002. Data integration: A theoretical perspective. In 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. 233--246. Google ScholarDigital Library
- P. LePendu, S. V. Iyer, A. Bauer-Mehren, R. Harpaz, J. M. Mortensen, T. Podchiyska, T. A. Ferris, and N. H. Shah. 2013. Pharmacovigilance using clinical notes. Clinical Pharmacology and Therapeutics 93, 6 (2013), 547--555.Google ScholarCross Ref
- V. I. Levenshtein. 1966. Binary codes capable of correcting deletions, insertions and reversals. In Soviet Physics Doklady, Vol. 10. 707--710.Google Scholar
- M. A. Levin, M. Krol, A. M. Doshi, and D. L. Reich. 2007. Extraction and mapping of drug names from free text to a standardized nomenclature. In AMIA Annual Symposium Proceedings. 438--442.Google Scholar
- A. Y. Levy. 2000. Logic-based techniques in data integration. In Logic-Based Artificial Intelligence, Kluwer International Series in Engineering and Computer Science, Vol. 597. Kluwer Academic Publishers, Norwell, MA. 575--595. Google ScholarDigital Library
- J. Li, H. Shen, and R. Topor. 2001. Mining optimal class association rule set. In Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 364--375. Google ScholarDigital Library
- M. Lindquist. 2008. VigiBase, the WHO Global ICSR database system: Basic facts. Drug Information Journal 42, 5 (2008), 409--419.Google ScholarCross Ref
- M. Lindquist, M. Ståhl, A. Bate, I. R. Edwards, and R. Meyboom. 2000. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Safety 23, 6 (2000), 533--542.Google ScholarCross Ref
- J. Liu, A. Li, and S. Seneff. 2011. Automatic drug side effect discovery from online patient-submitted reviews: Focus on statin drugs. In International Conference on Advances in Information Mining and Management. 91--96.Google Scholar
- M. Liu, E. R. McPeek Hinz, M. E. Matheny, J. C. Denny, J. S. Schildcrout, R. A. Miller, and H. Xu. 2013. Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. Journal of the American Medical Informatics Association 20, 3 (2013), 420--426.Google ScholarCross Ref
- X. Liu and H. Chen. 2013. AZDrugMiner: An information extraction system for mining patient-reported adverse drug events in online patient forums. In International Conference on Smart Health. 134--150. Google ScholarDigital Library
- J. J. Mao, A. Chung, A. Benton, S. Hill, L. Ungar, C. E. Leonard, S. Hennessy, and J. H Holmes. 2013. Online discussion of drug side effects and discontinuation among breast cancer survivors. Pharmacoepidemiology and Drug Safety 22, 3 (2013), 256--262.Google ScholarCross Ref
- A. T. McCray, S. Srinivasan, and A. C. Browne. 1994. Lexical methods for managing variation in biomedical terminologies. In The Annual Symposium on Computer Application in Medical Care. 235--239.Google Scholar
- T. L. McDonald, W. P. Erickson, and L. L. McDonald. 2000. Analysis of count data from before-after control-impact studies. Journal of Agricultural, Biological, and Environmental Statistics 5, 3 (2000), 262--279.Google ScholarCross Ref
- C. A. Naranjo, U. Busto, E. M. Sellers, P. Sandor, I. Ruiz, E. A. Roberts, E. Janecek, C. Domecq, and D. J. Greenblatt. 1981. A method for estimating the probability of adverse drug reactions. Clinical Pharmacology and Therapeutics 30 (1981), 239--245.Google ScholarCross Ref
- J. Nelson, A. Cook, and O. Yu. 2009. Evaluation of Signal Detection Methods for Use in Prospective Post-licensure Medical Product Safety Surveillance. Technical Report. FDA Sentinel Initiative Safety Signal Identification Contract.Google Scholar
- J. N. Nkanza and W. Walop. 2004. Vaccine associated adverse event surveillance (VAAES) and quality assurance. Drug Safety 27, 12 (2004), 951--952.Google Scholar
- G. N. Norén, J. Hopstadius, A. Bate, K. Star, and I. R. Edwards. 2010. Temporal pattern discovery in longitudinal electronic patient records. Data Mining and Knowledge Discovery 20, 3 (2010), 361--387. Google ScholarDigital Library
- G. N. Norén, R. Orre, and A. Bate. 2005. A hit-miss model for duplicate detection in the WHO drug safety database. In 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. 459--468. Google ScholarDigital Library
- G. N. Norén, R. Orre, A. Bate, and I. R. Edwards. 2007. Duplicate detection in adverse drug reaction surveillance. Data Mining and Knowledge Discovery 14, 3 (2007), 305--328. Google ScholarDigital Library
- S. Olsson, S. Pal, A. Stergachis, and M. Couper. 2010. Pharmacovigilance activities in 55 low- and middle-income countries. Drug Safety 33, 8 (2010), 689--703.Google ScholarCross Ref
- S. N. Pal, C. Duncombe, D. Falzon, and S. Olsson. 2013. WHO strategy for collecting safety data in public health programmes: Complementing spontaneous reporting systems. Drug Safety 36, 2 (2013), 75--81.Google ScholarCross Ref
- M. Y. Park, D. Yoon, K. Lee, S. Y. Kang, I. Park, S. H. Lee, W. Kim, H. J. Kam, Y. H. Lee, J. H. Kim, and R. W. Park. 2011. A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database. Pharmacoepidemiology and Drug Safety 20, 6 (2011), 598--607.Google ScholarCross Ref
- V. N. Patel and D. C. Kaelber. 2013. Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: A case study of azathioprine. Journal of Biomedical Informatics 52 (2013), 36--42.Google ScholarDigital Library
- M. Paul and M. Dredze. 2011. You are what you tweet: Analyzing twitter for public health. In International AAAI Conference on Weblogs and Social Media. 265--272.Google Scholar
- J. Pearl. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. Google ScholarDigital Library
- L. Peters, J. E. Kapusnik-Uner, T. Nguyen, and O. Bodenreider. 2011. An approximate matching method for clinical drug names. In AMIA Annual Symposium Proceedings, Vol. 2011. 1117--1126.Google Scholar
- L. Philips. 1990. Hanging on the metaphone. Computer Language 7, 12 (1990), 39--44.Google Scholar
- B. Quilitz and U. Leser. 2008. Querying distributed RDF data sources with SPARQL. In Proceedings of the 5th European Semantic Web Conference on the Semantic Web: Research and Applications. Canary Islands, Spain. 524--538. Google ScholarDigital Library
- E. Rahm and P. A. Bernstein. 2001. A survey of approaches to automatic schema matching. VLDB Journal 10, 4 (2001), 334--350. Google ScholarDigital Library
- E. Ramirez, A. J. Carcas, A. M. Borobia, S. H. Lei, E. Piñana, S. Fudio, and J. Frias. 2009. A pharmacovigilance program from laboratory signals for the detection and reporting of serious adverse drug reactions in hospitalized patients. Clinical Pharmacology and Therapeutics 87, 1 (2009), 74--86.Google ScholarCross Ref
- J. M. Reps, J. M. Garibaldi, U. Aickelin, D. Soria, J. E. Gibson, and R. B. Hubbard. 2012. Comparing data-mining algorithms developed for longitudinal observational databases. In 12th UK Workshop on Computational Intelligence. 1--8.Google Scholar
- J. M. Reps, J. M. Garibaldi, U. Aickelin, D. Soria, J. Gibson, and R. Hubbard. 2013. Comparison of algorithms that detect drug side effects using electronic healthcare databases. Soft Computing 17, 12 (2013), 2381--2397. Google ScholarDigital Library
- E. E. Roughead and S. J. Semple. 2009. Medication safety in acute care in Australia: Where are we now? Part 1: A review of the extent and causes of medication problems 2002--2008. Australia and New Zealand Health Policy 6, 1 (2009), 18.Google ScholarCross Ref
- T. Sakaeda, A. Tamon, K. Kadoyama, and Y. Okuno. 2013. Data mining of the public version of the FDA adverse event reporting system. International Journal of Medical Sciences 10, 7 (2013), 796--803.Google ScholarCross Ref
- M. Samwald, A. Jentzsch, C. Bouton, C. Kallesøe, E. Willighagen, J. Hajagos, M. Marshall, E. Prud’hommeaux, O. Hassanzadeh, and E. Pichler. 2011. Linked open drug data for pharmaceutical research and development. Journal of Cheminformatics 3, 19 (2011), 6 pages.Google ScholarCross Ref
- S. Sarawagi and A. Bhamidipaty. 2002. Interactive deduplication using active learning. In 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 269--278. Google ScholarDigital Library
- N. Savage. 2012. Digging for drug facts. Communications of the ACM 55, 10 (2012), 11--13. Google ScholarDigital Library
- D. Scanfeld, V. Scanfeld, and E. Larson. 2010. Dissemination of health information through social networks: Twitter and antibiotic. American Journal of Infection Control 3, 38 (2010), 182--188.Google ScholarCross Ref
- B. A. Slade, L. Leidel, C. Vellozzi, E. J. Woo, W. Hua, A. Sutherland, H. S. Izurieta, R. Ball, N. Miller, and M. M. Braun. 2009. Postlicensure safety surveillance for quadrivalent human papillomavirus recombinant vaccine. Journal of the American Medical Association 302, 7 (2009), 750--757.Google ScholarCross Ref
- P. E. Stang, P. B. Ryan, J. A. Racoosin, J. M. Overhage, A. G. Hartzema, C. Reich, E. Welebob, T. Scarnecchia, and J. Woodcock. 2010. Advancing the science for active surveillance: Rationale and design for the Observational Medical Outcomes Partnership. Annals of Internal Medicine 153, 9 (2010), 600--606.Google ScholarCross Ref
- M. Stephens, D. Buckingham, J. C. Talbot, and P. Routledge. 1985. The Detection of New Adverse Drug Reactions. Stockton Press.Google Scholar
- B. H. Stricker and J. G. Tijssen. 1992. Serum sickness-like reactions to cefaclor. Journal of Clinical Epidemiology 45, 10 (1992), 1177--1184.Google ScholarCross Ref
- M. Suling and I. Pigeot. 2012. Signal detection and monitoring based on longitudinal healthcare data. Pharmaceutics 4, 4 (2012), 607--640.Google ScholarCross Ref
- The Yellow Card Scheme. 2013. Homepage. Retrieved from https://yellowcard.mhra.gov.uk/.Google Scholar
- Petra A. Thrmann. 2001. Methods and systems to detect adverse drug reactions in hospitals. Drug Safety 24, 13 (2001), 961--968. DOI:http://dx.doi.org/10.2165/00002018-200124130-00003Google ScholarCross Ref
- A. Tinoco, R. S. Evans, C. J. Staes, J. F. Lloyd, J. M. Rothschild, and P. J. Haug. 2011. Comparison of computerized surveillance and manual chart review for adverse events. Journal of the American Medical Informatics Association 18, 4 (2011), 491--497.Google ScholarCross Ref
- Y. Tsuruoka, J. McNaught, and S. Ananiadou. 2008. Normalizing biomedical terms by minimizing ambiguity and variability. BMC Bioinformatics 9, 3 (2008), 1--10.Google ScholarCross Ref
- V. E. Valkhoff, P. M. Coloma, G. M. Masclee, R. Gini, F. Innocenti, F. Lapi, M. Molokhia, M. Mosseveld, M. S. Nielsson, M. Schuemie, F. Thiessard, J. van der Lei, M. C. Sturkenboom, G. Trifiró, and EU-ADR Consortium. 2014. Validation study in four health-care databases: Upper gastrointestinal bleeding misclassification affects precision but not magnitude of drug-related upper gastrointestinal bleeding risk. Journal of Clinical Epidemiology 67, 8 (2014), 921--931.Google ScholarCross Ref
- W. Wang, K. Haerian, H. Salmasian, R. Harpaz, H. Chase, and C. Friedman. 2011. A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from Pubmed citations. In AMIA Annual Symposium Proceedings. Washington, DC, 1464--1470.Google Scholar
- X. Wang, G. Hripcsak, and C. Friedman. 2009a. Characterizing environmental and phenotypic associations using information theory and electronic health records. BMC Bioinformatics 10, 9 (2009), S13.Google ScholarCross Ref
- X. Wang, G. Hripcsak, M. Markatou, and C. Friedman. 2009b. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: A feasibility study. Journal of the American Medical Informatics Association 16, 3 (2009), 328--337.Google ScholarCross Ref
- R. W. White, N. P. Tatonetti, N. H. Shah, R. B. Altman, and E. Horvitz. 2013. Web-scale pharmacovigilance: Listening to signals from the crowd. Journal of the American Medical Informatics Association 20, 3 (2013), 404--408.Google ScholarCross Ref
- G. Williams, R. Baxter, C. Kelman, C. Rainsford, H. He, L. Gu, D. Vickers, and S. Hawkins. 2002. Estimating episodes of care using linked medical claims data. In Advances in Artificial Intelligence. 660--671. Google ScholarDigital Library
- A. M. Wilson, L. Thabane, and A. Holbrook. 2004. Application of data mining techniques in pharmacovigilance. British Journal of Clinical Pharmacology 57, 2 (2004), 127--134.Google ScholarCross Ref
- R. L. Woosley. 2013. One hundred years of drug regulation: Where do we go from here? Annual Review of Pharmacology and Toxicology 53 (2013), 255--273.Google Scholar
- L. Wuerdeman, L. Volk, L. Pizziferri, R. Tsurikova, C. Harris, R. Feygin, M. Epstein, K. Meyers, J. S. Wald, and D. Lansky. 2005. How accurate is information that patients contribute to their electronic health record? In AMIA Annual Symposium Proceedings, Vol. 2005. 834.Google Scholar
- H. Xu, S. P. Stenner, S. Doan, K. B. Johnson, L. R. Waitman, and J. C. Denny. 2010. MedEx: A medication information extraction system for clinical narratives. Journal of the American Medical Informatics Association 17, 1 (2010), 19--24.Google ScholarCross Ref
- C. C. Yang, L. Jiang, H. Yang, and X. Tang. 2012a. Detecting signals of adverse drug reactions from health consumer contributed content in social media. In ACM SIGKDD Workshop on Health Informatics.Google Scholar
- C. Yang, P. Srinivasan, and P. M. Polgreen. 2012b. Automatic adverse drug events detection using letters to the editor. In AMIA Annual Symposium Proceedings. 1030--1039.Google Scholar
- A. Yates and N. Goharian. 2013. ADRTrace: Detecting expected and unexpected adverse drug reactions from user reviews on social media sites. Advances in Information Retrieval 7814 (2013), 816--819. Google ScholarDigital Library
- E. Yom-Tov and E. Gabrilovich. 2013. Postmarket drug surveillance without trial costs: Discovery of adverse drug reactions through large-scale analysis of web search queries. Journal of Medical Internet Research 15, 6 (2013), e124.Google ScholarCross Ref
- Q. Zeng, S. Kogan, N. Ash, R. A. Greenes, and A. A. Boxwala. 2002. Characteristics of consumer terminology for health information retrieval. Methods of Information in Medicine 41, 4 (2002), 289--298.Google ScholarCross Ref
Index Terms
- Text and Data Mining Techniques in Adverse Drug Reaction Detection
Recommendations
Social media mining for drug safety signal detection
SHB '12: Proceedings of the 2012 international workshop on Smart health and wellbeingAdverse Drug Reactions (ADRs) represent a serious problem all over the world. They may complicate a patient's medical conditions and increase the morbidity, even mortality. Drug safety currently depends heavily on post-marketing surveillance, because ...
Mining for Adverse Drug Events on Twitter
IC3K 2014: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1At the post-marketing phase when drugs are used by large populations and for long periods, unexpected adverse events may occur altering the risk-benefit relation of drugs, sometimes requiring a regulatory action. These events at the post-marketing phase ...
Analysis of adverse drug reactions using drug and drug target interactions and graph-based methods
Objective: The purpose of this study was to integrate knowledge about drugs, drug targets, and topological methods. The goals were to build a system facilitating the study of adverse drug events, to make it easier to find possible explanations, and to ...
Comments