A functional temporal association mining approach for screening potential drug–drug interactions from electronic patient databases

References

• Hines LE, Murphy JE, Grizzle AJ, Malone DC. Critical issues associated with drug-drug interactions: Highlights of a multistakeholder conference. American Journal of Health-System Pharmacy: AJHP: Official Journal of the American Society of Health-System Pharmacists 2011;68:941–6.
   Google Scholar
• Juurlink DN, Mamdani M, Kopp A, Laupacis A, Redelmeier DA. Drug-drug interactions among elderly patients hospitalized for drug toxicity. JAMA: The Journal of the American Medical Association 2003;289:1652–8.
   PubMed Web of Science ®Google Scholar
• Hamilton RA, Briceland LL, Andritz MH. Frequency of hospitalization after exposure to known drug–drug interactions in a Medicaid population. Pharmacotherapy 1998;18:1112–20.
   PubMed Web of Science ®Google Scholar
• Pirmohamed M, James S, Meakin S, Green C, Scott AK, Walley TJ, et al. Adverse drug reactions as cause of admission to hospital: Prospective analysis of 18 820 patients. BMJ (Clinical Research Ed) 2004;329:15–9.
   PubMed Web of Science ®Google Scholar
• Jankel CA, Fitterman LK. Epidemiology of drug–drug interactions as a cause of hospital admissions. Drug Safety: An International Journal of Medical Toxicology and Drug Experience 1993;9:51–9.
   PubMed Web of Science ®Google Scholar
• Troiano L, Scibelli G, Birtolo C. A fast algorithm for mining rare itemsets. Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications: Pisa (Italy); IEEE Computer Society; 2009. 1149–55 pp.
   Google Scholar
• So-Ngern A, Montakantikul P, Manosuthi W. Clinically significant drug interactions among HIV-infected patients receiving antiretroviral therapy. The Southeast Asian Journal of Tropical Medicine and Public Health 2014;45:1023–31.
   PubMed Web of Science ®Google Scholar
• Ahmad A, Khan MU, Haque I, Ivan R, Dasari R, Revanker M, et al. Evaluation of potential drug–drug interactions in general medicine ward of teaching hospital in southern India. Journal of Clinical and Diagnostic Research: JCDR 2015;9: FC10–3.
   Google Scholar
• Ellis JJ, Sadosky AB, Ten Eyck LL, Mudumby P, Cappelleri JC, Ndehi L, et al. A retrospective, matched cohort study of potential drug–drug interaction prevalence and opioid utilization in a diabetic peripheral neuropathy population initiated on pregabalin or duloxetine. BMC Health Services Research 2015;15:159.
   PubMed Web of Science ®Google Scholar
• Almenoff JS, DuMouchel W, Kindman LA, Yang X, Fram D. Disproportionality analysis using empirical Bayes data mining: A tool for the evaluation of drug interactions in the post-marketing setting. Pharmacoepidemiology and Drug Safety 2003;12:517–21.
   PubMed Web of Science ®Google Scholar
• Hartmann K, Doser AK, Kuhn M. Postmarketing safety information: How useful are spontaneous reports? Pharmacoepidemiology & Drug Safety 1999;8:65–71.
   Web of Science ®Google Scholar
• Hazell L, Shakir SA. Under-reporting of adverse drug reactions: A systematic review. Drug Safety: An International Journal of Medical Toxicology and Drug Experience 2006;29:385–96.
   PubMed Web of Science ®Google Scholar
• Goldman SA. Limitations and strengths of spontaneous reports data. Clinical Therapeutics 1998;20( Suppl C):C40–4.
   Google Scholar
• Biriell C, Edwards IR. Reasons for reporting adverse drug reactions–some thoughts based on an international review. Pharmacoepidemiol Drug Safety 1997;6:21–6.
   PubMed Web of Science ®Google Scholar
• Noren GN, Sundberg R, Bate A, Edwards IR. A statistical methodology for drug–drug interaction surveillance. Statistics in Medicine 2008;27:3057–70.
   PubMed Web of Science ®Google Scholar
• Huang Y-T, Lin S-F, Chiu C-C, Yeh H-Y, Soo V-W. Probability analysis on associations of adverse drug events with drug–drug interactions. Proceedings of the IEEE 7th International Conference on Bioinformatics and Bioengineering (BIBE). 2007; Boston. Boston: IEEE; 2007. 1308–12 pp.
   Google Scholar
• Tatonetti NP, Fernald GH, Altman RB. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. Journal of the American Medical Informatics Association: JAMIA 2012;19:79–85.
   PubMed Web of Science ®Google Scholar
• Hauben M. Early postmarketing drug safety surveillance: Data mining points to consider. The Annals of Pharmacotherapy 2004;38:1625–30.
   PubMed Web of Science ®Google Scholar
• Agrawal R, Srikant R. Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Databases. Santiago, Chile; 1994. 487–99 pp.
   Google Scholar
• Zhang C, Zhang S. Association rule mining: Models and algorithms. New York (NY): Springer-Verlag; 2002.
   Google Scholar
• Geng L, Hamilton HJ. Interestingness measures for data mining: A survey. ACM Computing Surveys 2006;38(3);9.
   Google Scholar
• Roddick JF, Spiliopoulou M. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 2002;14:750–67.
   Web of Science ®Google Scholar
• Tan P-N, Steinbach M, Kumar V. Introduction to data mining. Boston (MA); Addison Wesley; 2005.
   Google Scholar
• Norén GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Mining and Knowledge Discovery 2010;20:361–87.
   Web of Science ®Google Scholar
• Jin H, Chen J, He H, Williams G, Kelman C, O’Keefe C. Mining unexpected temporal associations: Applications in detecting adverse drug reactions. IEEE Transactions on Information Technology in Biomedicine 2008;12:488–500.
   PubMedGoogle Scholar
• Sacchi L, Larizza C, Combi C, Bellazzi R. Data mining with temporal abstractions: Learning rules from time series. Data Mining and Knowledge Discovery 2007;15:217–47.
   Web of Science ®Google Scholar
• Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R. Mining health care administrative data with temporal association rules on hybrid events. Methods of Information in Medicine 2011;50:166–79.
   PubMed Web of Science ®Google Scholar
• Patnaik D, Butler P, Ramakrishnan N, Parida L, Keller BJ, Hanauer DA. Experiences with mining temporal event sequences from electronic medical records: Initial successes and some challenges. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego (CA): ACM; 2011. 360–8 pp.
   Google Scholar
• Fei W, Lee N, Jianying H, Jimeng S, Ebadollahi S, Laine AF. A framework for mining signatures from event sequences and its applications in healthcare data. IEEE Transactions on Pattern Analysis and Machine Intelligence 2013;35:272–85.
   PubMed Web of Science ®Google Scholar
• Jin H, Chen J, Chris K, He H, Damien M, OkC M. Mining unexpected associations for signalling potential adverse drug reactions from administrative health databases. 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). Singapore; 2006. 867–76 pp.
   Google Scholar
• Noren GN, Bate A, Hopstadius J, Star K, Edwards IR. Temporal pattern discovery for trends and transient effects: Its application to patient records. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, NV, USA: ACM; 2008. 963–71 pp.
   Google Scholar
• Concaro S, Sacchi L, Cerra C, Bellazzi R. Mining administrative and clinical diabetes data with temporal association rules. Studies in Health Technology and Informatics 2009;150:574–8.
   PubMedGoogle Scholar
• Lotfi S, Sadreddini MH. Mining Fuzzy association rules using mutual information. Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 (IMECS 2009). Hong Kong; 2009.
   Google Scholar
• AHRQ. Clinical Classifications Software (CCS) for ICD-9-CM fact sheet. Rockville (MD); Healthcare Cost and Utilization Project (HCUP); 2007.
   Google Scholar
• Alshekhlee A, Horn C, Jung R, Alawi AA, Cruz-Flores S. In-hospital mortality in acute ischemic stroke treated with hemicraniectomy in U.S. hospitals. Journal of Stroke and Cerebrovascular Diseases: The Official Journal of National Stroke Association 2011;20:196–201.
   PubMed Web of Science ®Google Scholar
• Dinan MA, Chou CH, Hammill BG, Graham FL, Schulman KA, Telen MJ, et al. Outcomes of inpatients with and without sickle cell disease after high-volume surgical procedures. American Journal of Hematology 2009;84:703–9.
   PubMed Web of Science ®Google Scholar
• Ash AS, Posner MA, Speckman J, Franco S, Yacht AC, Bramwell L. Using claims data to examine mortality trends following hospitalization for heart attack in Medicare. Health Services Research 2003;38:1253–62.
   PubMed Web of Science ®Google Scholar
• Glassman PA, Simon B, Belperio P, Lanto A. Improving recognition of drug interactions: Benefits and barriers to using automated drug alerts. Medical Care 2002;40:1161–71.
   PubMed Web of Science ®Google Scholar
• Ling Y, An Y, Liu M, Hu X. An error detecting and tagging framework for reducing data entry errors in electronic medical records (EMR) system. 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2013. 249–54 pp.
   Google Scholar
• Hauben M, Zhou X. Quantitative methods in pharmacovigilance: Focus on signal detection. Drug Safety 2003;26:159–86.
   PubMed Web of Science ®Google Scholar