A Multiagent System for Integrated Detection of Pharmacovigilance Signals
详细信息 查看全文
- 作者：Vassilis Koutkias ; Marie-Christine Jaulent
- 关键词：Pharmacovigilance ; Computational signal detection methods ; Heterogeneous data sources ; Multiagent system ; Aggregation and reasoning scheme
- 刊名：Journal of Medical Systems
- 出版年：2016
- 出版时间：February 2016
- 年：2016
- 卷：40
- 期：2
- 全文大小：1,280 KB
- 参考文献：1.WHO, The Importance of Pharmacovigilance: Safety Monitoring of Medicinal Products. World Health Organization, Geneva, CH (2002)
2.Grootheest, A., and Richesson, R.: Pharmacovigilance. In: Richesson, R., and Andrews, J. (Eds.) In: Clinical Research Informatics, Health Informatics, pp. 367–387. Springer, London (2012), doi:10.1007/978-1-84882-448-5_19
3.Linguist, M., The need for definitions in pharmacovigilance. Drug Saf 30(10):825–830, 2007. doi:10.2165/00002018200730100-00001 .CrossRef
4.Council for International Organizations of Medical Sciences, Practical Aspects of Signal Detection in Pharmacovigilance. Report of CIOMS Working Group VIII. CIOMS, Geneva, CH (2010)
5.WHO, A Practical Handbook on the Pharmacovigilance of Antimalarial Medicines. World Health Organization, Geneva, CH (2008)
6.Kelman, C.W., et al., Evaluating medicines: let’s use all the evidence. Med J Aust 186(5):249–252, 2007.PubMed
7.Trifirò, G., et al., Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor. Pharmacoepidemiol Drug Saf 18(12):1176–1184, 2009. doi:10.1002/pds.1836 .CrossRef PubMed
8.Gurulingappa, H., Toldo, L., Rajput, A.M., Kors, J.A., Taweel, A., Tayrouz, Y., Automatic detection of adverse events to predict drug label changes using text and data mining techniques. Pharmacoepidemiol Drug Saf 22(11):1189–1194, 2013. doi:10.1002/pds.3493 .CrossRef PubMed
9.Sarker, A., et al., Utilizing social media data for pharmacovigilance: a review. J. Biomed. Inform. 54: 202–212, 2015. doi:10.1016/j.jbi.2015.02.004 .CrossRef PubMed
10.Harpaz, R., et al., Novel data mining methodologies for adverse drug event discovery and analysis. Clin. Pharmacol. Ther. 91(6):1010–1021, 2012. doi:10.1038/clpt.2012.50 .PubMedCentral CrossRef PubMed
11.Hauben, M., and Norén, G.N., A decade of data mining and still counting. Drug Saf 33(7):527–534, 2010. doi:10.2165/11532430-000000000-00000 .CrossRef PubMed
12.Shang, N., Xu, H., Rindflesch, T.C., Cohen, T., Identifying plausible adverse drug reactions using knowledge extracted from the literature. J. Biomed. Inform. 52:293–310, 2014. doi:10.1016/j.jbi.2014.07.011 .PubMedCentral CrossRef PubMed
13.Koutkias, V.G., and Jaulent, M.-C., Computational approaches for pharmacovigilance signal detection: toward integrated and semantically-enriched frameworks. Drug Saf 38(3):219–232, 2015. doi:10.1007/s40264-015-0278-8 .PubMedCentral CrossRef PubMed
14.FDA Adverse Event Reporting System (FAERS). http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ . Accessed 13 September 2015
15.EudraVigilance. https://eudravigilance.ema.europa.eu/ . Accessed 13 September 2015
16.VigiBase®. http://www.umc-products.com/ . Accessed 13 September 2015
17.Hauben, M., and Bate, A., Decision support methods for the detection of adverse events in post-marketing data. Drug Discov Today 14(7-8):343–357, 2009. doi:10.1016/j.drudis.2008.12.012 .CrossRef PubMed
18.Chazard, E., Ficheur, G., Bernonville, S., Luyckx, M., Beuscart, R., Data mining to generate adverse drug events detection rules. IEEE Trans. Inf. Technol. Biomed. 15 (6): 823–830, 2011. doi:10.1109/TITB.2011.2165727 .CrossRef PubMed
19.Reps, J., Garibaldi, J., Aickelin, U., Soria, D., Gibson, J., Hubbard, R., Comparison of algorithms that detect drug side effects using electronic healthcare database. Soft. Comput. 17:2381–2397, 2013. doi:10.1007/s00500-013-1097-4 .CrossRef
20.Madigan, D., Schuemie, M.J., Ryan, P.B., Empirical performance of the case-control design: lessons for developing a risk identification and analysis system. Drug Saf 36:S73–S82, 2013. doi:10.1007/s40264-013-0105-z .CrossRef PubMed
21.Ryan, P.B., Schuemie, M.J., Gruber, S., Zorych, I., Madigan, D., Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system. Drug Saf 36:S59–S72, 2013. doi:10.1007/s40264-013-0099-6 .CrossRef PubMed
22.Suchard, M.A., Zorych, I., Simpson, S.E., Schuemie, M.J., Ryan, P.B., Madigan, D., Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system. Drug Saf 36:S83–S93, 2013. doi:10.1007/s40264-013-0100-4 .CrossRef PubMed
23.Noreń, N.G., Hopstadius, J., Bate, A., Star, K., Edwards, R.I., Temporal pattern discovery in longitudinal electronic patient records. Data Min. Knowl. Discov. 20:361–387, 2010. doi:10.1007/s10618-009-0152-3 .CrossRef
24.The Observational Medical Outcomes Partnership. http://omop.org/. Accessed 13 September 2015
25.LePendu, P., et al., Pharmacovigilance using clinical notes. Clin Pharmacol Ther 93:547–555, 2013. doi:10.1038/clpt.2013.47 .CrossRef PubMed
26.Freifeld, C.C., et al., Digital drug safety surveillance: monitoring pharmaceutical products in Twitter. Drug Saf 37:343–350, 2014. doi:10.1007/s40264-014-0155-x .PubMedCentral CrossRef PubMed
27.Harpaz, R., et al., Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf 37:777–790, 2014. doi:10.1007/s40264-014-0218-z .PubMedCentral CrossRef PubMed
28.Harpaz, R., DuMouchel, W., LePendu, P., Bauer-Mehren, A., Ryan, P., Shah, N.H., Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system. Clin. Pharmacol. Ther. 93:539–546, 2013. doi:10.1038/clpt.2013.24 .CrossRef PubMed
29.Liu, M., et al., Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. J. Am. Med. Inform. Assoc. 20:420–426, 2013. doi:10.1136/amiajnl-2012-001119 .PubMedCentral CrossRef PubMed
30.Schuemie, M.J., et al., Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Med. Care. 50:890–897, 2012. doi:10.1097/MLR.0b013e31825f63bf .CrossRef PubMed
31.van Holle, L., and Bauchau, V., Signal detection on spontaneous reports of adverse events following immunisation: a comparison of the performance of a disproportionality-based algorithm and a time-to-onset-based algorithm. Pharmacoepidemiol Drug Saf 23:178–185, 2014. doi:10.1002/pds.3502 .PubMedCentral CrossRef PubMed
32.Harpaz, R., et al., Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J. Am. Med. Inform. Assoc. 20(3):413–419, 2013. doi:doi:10.1136/amiajnl-2012-000930 doi:10.1136/amiajnl-2012-000930 .PubMedCentral CrossRef PubMed
33.Stang, P., et al., Variation in choice of study design: findings from the epidemiology design decision inventory and evaluation (EDDIE) survey. Drug Saf 36:S15–S25, 2013. doi:10.1007/s40264-013-0103-1 .CrossRef PubMed
34.Koutkias, V.G., and Jaulent, M.-C.: Leveraging post-marketing drug safety research through semantic technologies: the PharmacoVigilance Signal Detectors Ontology.. In: Proceedings of the 7th International Workshop on Semantic Web Applications and Tools for Life Sciences, CEUR Workshop Proceedings, Vol. 1320, Berlin, Germany, December 9–11 (2014)
35.Singh, M.P., and Huhns, M.N., Service-Oriented Computing: Semantics, Processes, Agents: Wiley, 2005.
36.Luck, M., McBurney, P., Preist, C., A manifesto for agent technology: towards next generation computing. Auton. Agent. Multi-Ag. 9(3):203–252, 2004. doi:10.1023/B:AGNT.0000038027.29035.7c .CrossRef
37.Isern, D, Sánchez, D., Moreno, A., Agents applied in health care: a review. Int. J. Med. Inform. 79(3): 145–166, 2010. doi:10.1016/j.ijmedinf.2010.01.003 .CrossRef PubMed
38.Klusch, M., and Sycara, K.: Brokering and matchmaking for coordination of agent societies: a survey. In: Omicini, A., Zambonelli, F., Klusch, M., Tolksdorf, R. (Eds.) In: Coordination of Internet Agents, pp. 197–224. Springer-Verlag (2001)
39.Durfee, E.H.: Distributed Problem Solving and Planning. In: Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA, MIT Press, 2000, pp. 121– 164
40.Tran, Q.-N.N., and Low, G., MOBMAS: a methodology for ontology-based multi-agent systems development. Inform. Soft. Tech. 50(7-8):697–722, 2008. doi:10.1016/j.infsof.2007.07.005 .CrossRef
41.Lindquist, M., Use of triage strategies in the WHO signal-detection process. Drug Saf 30(7):635–637, 2007. doi:10.2165/00002018-200730070-00014 .CrossRef PubMed
42.Kuhn, M., Campillos, M., Letunic, I., Juhl Jensen, L., Bork, P., A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(1), 2010. doi:10.1038/msb.2009.98 .
43.The DrugBank database. http://www.drugbank.ca/ . Accessed 13 September 2015
44.The ChEMBL database. https://www.ebi.ac.uk/chembl/ . Accessed 13 September 2015
45.Ahmed, I., and Poncet, A., PhViD: An R package for PharmacoVigilance signal Detection. R package version 1.0.6 (2013)
46.OWL 2 Web Ontology Language: Structural Specification and Functional-Style Syntax, 2nd Ed. http://www.w3.org/TR/2012/REC-owl2-syntax-20121211/ . Accessed 13 September 2015
47.The openFDA Drug API. https://open.fda.gov/drug/event/ . Accessed 13 September 2015
48.Europe PubMed Central RESTful Web Service. http://europepmc.org/restfulwebservice . Accessed 13 September 2015
49.The Twitter REST APIs. https://dev.twitter.com/rest/public . Accessed 13 September 2015
50.SPARQL 1.1 Overview, W3C Recommendation, 21 March 2013. Accessed 13 September 2015 (2013). http://www.w3.org/TR/sparql11-overview/
51.bio2RDF. http://bio2rdf.org/ . Accessed 13 September 2015
52.Java Agent DEvelopment framework (JADE). http://jade.tilab.com/ . Accessed 13 September 2015
53.The Foundation for Intelligent Physical Agents (FIPA). http://www.fipa.org/ . Accessed 13 September 2015
54.Rserve - Binary R server. http://rforge.net/Rserve/ . Accessed 13 September 2015
55.Foundation for Intelligent Physical Agents, FIPA Abstract Architecture Specification, SC00001L, http://fipa.org/specs/fipa00001/SC00001L.pdf . Accessed 13 September 2015
56.Foundation for Intelligent Physical Agents, FIPA ACL Message Structure Specification, SC00061G, 03/12/2002. http://www.fipa.org/specs/fipa00061/SC00061G.pdf . Accessed 13 September 2015
57.Foundation for Intelligent Physical Agents, FIPA Communicative Act Library Specification, SC00037J, 03/12/2002. http://www.fipa.org/specs/fipa00037/SC00037J.pdf . Accessed 13 September 2015
58.Foundation for Intelligent Physical Agents, FIPA SL Content Language Specification, SC00008I, 03/12/2002. http://www.fipa.org/specs/fipa00008/SC00008I.pdf . Accessed 13 September 2015
59.Caire, G., and Cabanillas, D.: JADE Tutorial: Application-defined Content Languages and Ontologies., 15 April 2010. http://jade.tilab.com/doc/tutorials/CLOntoSupport.pdf . Accessed 13 September 2015
60.The clinical Text Analysis and Knowledge Extraction System (cTAKES). http://ctakes.apache.org/ . Accessed 13 September 2015
61.The Unified Medical Language System (UMLS). http://www.nlm.nih.gov/research/umls/ . Accessed 13 September 2015
62.The Unstructured Information Management Architecture (UIMA). http://uima.apache.org/ . Accessed 13 September 2015
63.The OMOP Methods Library. http://omop.org/MethodsLibrary . Accessed 13 September 2015
64.Norén, N.G., and Edwards, I.R., Modern methods of pharmacovigilance: detecting adverse effects of drugs. Clinical Medicine 9(5):486–489, 2009. doi:10.7861/clinmedicine.9-5-486 .CrossRef PubMed
65.Liu, M., et al., Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J. Am. Med. Inform. Assoc. 19(e1):28–35, 2012. doi:doi:10.1136/amiajnl-2011-000699 doi:10.1136/amiajnl-2011-000699 .CrossRef
66.The Mini-Sentinel project. http://www.mini-sentinel.org/ . Accessed 13 September 2015
67.Observational Health Data Sciences and Informatics (OHDSI) program. http://www.ohdsi.org/ . Accessed 13 September 2015
68.Oliveira, J.L., et al., The EU-ADR web platform: delivering advanced pharmacovigilance tools. Pharmacoepidemiol Drug Saf 22(5):459–467, 2013. doi:10.1002/pds.3375 .CrossRef PubMed
69.González-Vélez, H., et al., Healthagents: distributed multi-agent brain tumor diagnosis and prognosis. Appl. Intell. 30(3):191–202, 2009. doi:10.1007/s10489-007-0085-8 .CrossRef
70.Bromuri, S., Schumacher, M.I., Stathis, K., Ruiz, J.: Monitoring Gestational Diabetes Mellitus with Cognitive Agents and Agent Environments. In: Proc. of IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol. 2, pp. 409–414, Lyon, France, Aug. 22-27 (2011), doi:10.1109/WI-IAT.2011.37
71.Koutkias, V., Chouvarda, I., Maglaveras, N., A multiagent system enhancing home-care health services for chronic disease management. IEEE Trans. Inf. Technol. Biomed. 9 (4): 528–537, 2005. doi:10.1007/978-3-662-04401-8_8 .CrossRef PubMed
72.Kaluža, B., et al., A multi-agent care system to support independent living. Int. J. Artif. Intell. T. 23 (1), 2014. doi:10.1142/S0218213014400016 .
73.Koutkias, V.G., Malousi, A., Maglaveras, N., Engineering agent-mediated integration of bioinformatics analysis tools. Multiagent Grid Syst 3(2):245–258, 2007.
74.Bouzguenda, L., and Turki, M., Dynamic medical cross-organizational workflow management system. J Med Syst 38:32, 2014. doi:10.1007/s10916-014-0032-2 .CrossRef PubMed
75.Cortés, U., Vázquez-Salceda, J., López-Navidad, A., Caballero, F., UCTx: a multi-agent system to assist a transplant coordination unit. Appl. Intell. 20(1):59–70, 2004. doi:10.1023/B:APIN.0000011142.91514.57 .CrossRef
76.Tian, Y., Zhou, T.-S., Yao, Q., Zhang, M., Li, J.-S., Use of an agent-based simulation model to evaluate a mobile-based system for supporting emergency evacuation decision making. J Med Syst 38:149, 2014. doi:10.1007/s10916-014-0149-3 .CrossRef PubMed
77.Ji, Y., et al., A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition-primed decision model. Int. J. Intell. Syst. 22(8):827–845, 2007. doi:10.1002/int.20230 .CrossRef
78.Nyulas, C.I., O’Connor, M.J., Tu, S.W., Buckeridge, D.L., Okhmatovskaia, A., Musen, M.A.: An ontology-driven framework for deploying JADE agent systems. In: Proc. of IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT), Vol. 2, pp. 573–577, Sydney, Australia, Dec. 9-12 (2008), doi:10.1109/WIIAT.2008.25
79.Ferrucci, D., et al., Building Watson: an overview of the DeepQA project. AI Magazine 31(3):59–79, 2010. doi:10.1609/aimag.v31i3.2303 .
80.Santos, E., Wilkinson, J.T., Santos, E., Fusing multiple Bayesian knowledge sources. Int. J. Approx. Reason. 52(7):935–947, 2011. doi:10.1016/j.ijar.2011.01.008 .CrossRef
81.He, H., Cao, Y., Wen, J., Cheng, S.: A boost voting strategy for knowledge integration and decision making. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (Eds.) In: Advances in Neural Networks - ISNN 2008, volume 5263 of Lecture Notes in Computer Science, pp. 472–481. Springer, Berlin (2008), doi:10.1007/978-3-540-87732-5_53
82.DeployR. http://deployr.revolutionanalytics.com/ . Accessed 13 September 2015
83.Burian, P., Multi-agent Systems and Cloud Computing for Controlling and Managing Chemical and Food Processes. J. Chem. Chem. Eng. 6:1121–1135, 2012. - 作者单位：Vassilis Koutkias (1) (2) (3)
Marie-Christine Jaulent (1) (2) (3)
1. INSERM, U1142, LIMICS, 75006, Paris, France
2. Sorbonne Universités, UPMC University Paris 06, UMR_S 1142, LIMICS, 75006, Paris, France
3. Université Paris 13, Sorbonne Paris Cité, LIMICS, UMR_S 1142, 93430, Villetaneuse, France - 刊物类别：Mathematics and Statistics
- 刊物主题：Statistics
Statistics for Life Sciences, Medicine and Health Sciences
Health Informatics and Administration - 出版者：Springer Netherlands
- ISSN：1573-689X
文摘
Pharmacovigilance is the scientific discipline that copes with the continuous assessment of the safety profile of marketed drugs. This assessment relies on diverse data sources, which are routinely analysed to identify the so-called “signals”, i.e. potential associations between drugs and adverse effects, that are unknown or incompletely documented. Various computational methods have been proposed to support domain experts in signal detection. However, recent comparative studies illustrated that current methods exhibit high false-positive rates, significantly variable performance across different datasets used for analysis and events of interest, but also complementarity in their outcomes. In this regard, in order to reinforce accurate and timely signal detection, we elaborated through an agent-based approach towards systematic, joint exploitation of multiple heterogeneous signal detection methods, data sources and other drug-related resources under a common, integrated framework. The approach relies on a multiagent system operating based on a collaborative agent interaction protocol, aiming to implement a comprehensive workflow that comprises of method selection and execution, as well as outcomes’ aggregation, filtering, ranking and annotation. This paper presents the design of the proposed multiagent system, discusses implementation issues and demonstrates the applicability of the proposed solution in an example signal detection scenario. This work constitutes a step towards large-scale, integrated and knowledge-intensive computational signal detection.