research-article

Using Health-Consumer-Contributed Data to Detect Adverse Drug Reactions by Association Mining with Temporal Analysis

Authors:

Christopher C. YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 4

Article No.: 55, Pages 1 - 27

https://doi.org/10.1145/2700482

Published: 13 July 2015 Publication History

Abstract

Since adverse drug reactions (ADRs) represent a significant health problem all over the world, ADR detection has become an important research topic in drug safety surveillance. As many potential ADRs cannot be detected though premarketing review, drug safety currently depends heavily on postmarketing surveillance. Particularly, current postmarketing surveillance in the United States primarily relies on the FDA Adverse Event Reporting System (FAERS). However, the effectiveness of such spontaneous reporting systems for ADR detection is not as good as expected because of the extremely high underreporting ratio of ADRs. Moreover, it often takes the FDA years to complete the whole process of collecting reports, investigating cases, and releasing alerts. Given the prosperity of social media, many online health communities are publicly available for health consumers to share and discuss any healthcare experience such as ADRs they are suffering. Such health-consumer-contributed content is timely and informative, but this data source still remains untapped for postmarketing drug safety surveillance. In this study, we propose to use (1) association mining to identify the relations between a drug and an ADR and (2) temporal analysis to detect drug safety signals at the early stage. We collect data from MedHelp and use the FDA's alerts and information of drug labeling revision as the gold standard to evaluate the effectiveness of our approach. The experiment results show that health-related social media is a promising source for ADR detection, and our proposed techniques are effective to identify early ADR signals.

References

[1]

R. Agrawal, T. Imieliński, and A. Swami. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD Record. ACM, 207--216.

Digital Library

[2]

J. M. Ale and G. H. Rossi. 2000. An approach to discovering temporal association rules. In Proceedings of the 2000 ACM Symposium on Applied Computing-Volume 1. ACM, 294--300.

Digital Library

[3]

W.-H. Au and K. C. C. Chan. 2005. Mining changes in association rules: A fuzzy approach. Fuzzy Sets and Systems 149, 1 (2005), 87--104.

Digital Library

[4]

A. Benton, J. H. Holmes, S. Hill, A. Chung, and L. Ungar. 2012. medpie: An information extraction package for medical message board posts. Bioinformatics 28, 5 (2012), 743--744.

Digital Library

[5]

A. Benton, L. Ungar, S. Hill, S. Hennessy, J. Mao, A. Chung, 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.

Digital Library

[6]

M. Boettcher. 2011. Contrast and change mining. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery 1, 3 (2011), 215--230.

[7]

A. Cami, A. Arnold, S. Manzi, and B. Reis. 2011. Predicting adverse drug events using pharmacological network models. Science Translational Medicine 3, 114 (2011), 1--11.

[8]

B. W. Chee, R. Berlin, and B. Schatz. 2011. Predicting adverse drug events from personal health messages. In Proceedings of the AMIA Annual Symposium. 217.

[9]

X. Chen and I. Petrounias. 1998. A framework for temporal data mining. In Proceedings of the Database and Expert Systems Applications. Springer, 796--805.

Digital Library

[10]

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.

[11]

I. R. Edwards and J. K. Aronson. 2000. Adverse drug reactions: Definitions, diagnosis, and management. Lancet 356, 9237 (2000), 1255--1259.

[12]

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.

[13]

S. Fox and M. Duggan. 2013. Health Online 2013. Retrieved 5/20/2013, from http://www.pewinternet.org/2013/01/15/health-online-2013/.

[14]

T. F. Gharib, H. Nassar, M. Taha, and A. Abraham. 2010. An efficient algorithm for incremental mining of temporal association rules. Data & Knowledge Engineering 69, 8 (2010), 800--815.

Digital Library

[15]

J. Han, M. Kamber, and J. Pei. 2006. Data Mining: Concepts and Techniques. Elsevier.

Digital Library

[16]

M. Hauben. 2004. Early postmarketing drug safety surveillance: Data mining points to consider. Annals of Pharmacotherapy 38, 10 (2004), 1625--1630.

[17]

L. Hazell and S. A. W. Shakir. 2006. Under-Reporting of Adverse Drug Reactions: A Systematic Review, Vol. 29, pp. 385--385. New Zealand: Adis International.

[18]

A. M. Hochberg, S. J. Reisinger, R. K. Pearson, D. J. O'Hara, and K. Hall. 2007. Using data mining to predict safety actions from FDA adverse event reporting system data. Drug Information Journal 41, 5 (2007), 633.

[19]

C. Hofer-Dueckelmann, E. Prinz, W. Beindl, J. Szymanski, G. Fellhofer, M. Pichler, and J. Schuler. 2011. Adverse drug reactions (ADRs) associated with hospital admissions - elderly female patients are at highest risk. International Journal of Clinical Pharmacology and Therapeutics 49, 10 (2011), 577--586.

[20]

Y. Ji, Y. Hao, 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.

Digital Library

[21]

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.

Digital Library

[22]

Y. Ji, H. Ying, P. Dews, M. S. Farber, A. Mansour, J. Tran, and R. M. Massanari. 2010. A fuzzy recognition-primed decision model-based causal association mining algorithm for detecting adverse drug reactions in postmarketing surveillance. Paper presented at the 2010 IEEE International Conference on Fuzzy Systems (FUZZ). 1--8.

[23]

L. Jiang and C. Yang. 2013. Using co-occurrence analysis to expand consumer health vocabularies from social media data. Paper presented at the IEEE International Conference on Healthcare Informatics, Philadelphia, PA, September 8--11, 2013.

Digital Library

[24]

L. Jiang, C. C. Yang, and J. Li. 2013. Discovering consumer health expressions from consumer-contributed content. In Proceedings of the Social Computing, Behavioral-Cultural Modeling and Prediction. Springer, 164--174.

Digital Library

[25]

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.

Digital Library

[26]

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.

Digital Library

[27]

C. Kongkaew, P. R. Noyce, and D. M. Ashcroft. 2008. Hospital admissions associated with adverse drug reactions: A systematic review of prospective observational studies. Annals of Pharmacotherapy, 42, 7 (2008), 1017--1025.

[28]

S. Kotsiantis and D. Kanellopoulos. 2006. Association rules mining: A recent overview. GESTS International Transactions on Computer Science and Engineering 32, 1 (2006), 71--82.

[29]

K. Kubota, D. Koide, and T. Hirai. 2004. Comparison of data mining methodologies using Japanese spontaneous reports. Pharmacoepidemiology and Drug Safety 13, 6 (2004), 387--394.

[30]

K. L. Lanctot and C. A. Naranjo. 1994. Computer-assisted evaluation of adverse events using a Bayesian-approach. Journal of Clinical Pharmacology 34, 2 (1994), 142--147.

[31]

J. Lazarou, B. H. Pomeranz, and P. N. Corey. 1998. Incidence of adverse drug reactions in hospitalized patients: A meta-analysis of prospective studies. JAMA: Journal of the American Medical Association 279, 15 (1998), 1200--1205.

[32]

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. Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. ACL, 117--125.

Digital Library

[33]

C.-H. Lee, C.-R. Lin, and M.-S. Chen. 2001. On mining general temporal association rules in a publication database. In Proceedings of the IEEE International Conference on Data Mining (ICDM'01). IEEE, 337--344.

Digital Library

[34]

W.-J. Lee and S.-J. Lee. 2004. Discovery of fuzzy temporal association rules. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34, 6 (2004), 2330--2342.

Digital Library

[35]

Y. Li, P. Ning, X. S. Wang, and S. Jajodia. 2003. Discovering calendar-based temporal association rules. Data & Knowledge Engineering 44, 2 (2003), 193--218.

Digital Library

[36]

W.-Y. Lin, H.-Y. Li, J.-W. Du, W.-Y. Feng, C.-F. Lo, and V.-W. Soo. 2012. iADRs: Towards online adverse drug reaction analysis. SpringerPlus 1, 1 (2012), 1--16.

[37]

M. Lindquist, I. R. Edwards, A. Bate, H. Fucik, A. M. Nunes, and M. Stahl. 1999. From association to alert - A revised approach to international signal analysis. Pharmacoepidemiology and Drug Safety 1 (1999), 15--25.

[38]

X. Liu and H. Chen. 2013. AZDrugMiner: An information extraction system for mining patient-reported adverse drug events in online patient forums. Smart Health. Springer, 134--150.

Digital Library

[39]

Med Help. (1994, Last Updated 6/20/2011). Medical information, forums and communities: About us. Retrieved 1/29/2014, from http://www.medhelp.org/aboutus.htm.

[40]

A. Nikfarjam and G. H. Gonzalez. 2011. Pattern mining for extraction of mentions of adverse drug reactions from user comments. AMIA Annual Symposium Proceedings 2011. 1019--1026.

[41]

Y. Pouliot, A. P. Chiang, and A. J. Butte. 2011. Predicting adverse drug reactions using publicly available PubChem bioassay data. Clinical Pharmacology & Therapeutics 90, 1 (2011), 90--99.

[42]

J. F. Roddick and M. Spiliopoulou. 2002. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14, 4 (2002), 750--767.

Digital Library

[43]

K. Shanmugapriya, D. Shanmugapriya, H. S. Parveen, and V. Niranjani. 2011. N-Unexpected temporal association rule for diagnosing adverse drug reaction from health database. International Proceedings of Computer Science and Information Technology (IPCSIT'11). 18.

[44]

A. Szarfman, J. M. Tonning, and P. M. Doraiswamy. 2004. Pharmacovigilancein the 21st century: New systematic tools for an old problem. Pharmacotherapy 24, (2004), 1099--1104.

[45]

E. P. van Puijenbroek, A. C. G. Egberts, R. H. B. Meyboom, and H. G. M. Leufkens. 1999. Signalling possible drug--drug interactions in a spontaneous reporting system: Delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole. British Journal of Clinical Pharmacology 47, (1999), 689--693.

[46]

H. Wactlar, M. Pavel, and W. Barkis. 2011. Can computer science save healthcare? IEEE Intelligent Systems 26, 5 (2011), 79.

[47]

X. Wang, G. Hripcsak, M. Markatou, and C. Friedman. 2009. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: A feasibility study. JAMIA: Journal of the American Medical Informatics Association 16, 3 (2009), 328--337.

[48]

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.

[49]

E. Winarko and J. F. Roddick. 2007. ARMADA--an algorithm for discovering richer relative temporal association rules from interval-based data. Data & Knowledge Engineering 63, 1 (2007), 76--90.

Digital Library

[50]

A. J. J. Wood. 2000. Thrombotic thrombocytopenic purpura and clopidogrel—A need for new approaches to drug safety. New England Journal of Medicine 342, 24 (2000), 1824--1826.

[51]

C. C. Yang, L. Jiang, H. Yang, and X. Tang. 2012. Detecting signals of adverse drug reactions from health consumer contributed content in social media. Proceedings of ACM SIGKDD Workshop on Health Informatics, Beijing, August 12, 2012.

[52]

C. C. Yang, H. Yang, L. Jiang, and M. Zhang. 2012. Social media mining for drug safety signal detection. Proceedings of ACM CIKM International Workshop on Smart Health and Wellbeing, Maui, Hawaii, October 29, 2012.

Digital Library

[53]

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.

[54]

Q. Zeng, S. Kogan, N. Ash, and R. A. Greenes. 2001. Patient and clinician vocabulary: How different are they? Studies in Health Technology Information (2001), 399--403.

[55]

Q. T. Zeng and T. Tse. 2006. Exploring and developing consumer health vocabularies. JAMIA: Journal of the American Medical Informatics Association 13, 1 (2006), 24--29.

[56]

Q. T. Zeng, T. Tse, J. Crowell, G. Divita, L. Roth, and A. C. Browne. 2005. Identifying consumer-friendly display (CFD) names for health concepts. AMIA Annual Symposium Proceedings (2005), 859--863.

[57]

Q. T. Zeng, T. Tse, G. Divita, A. Keselman, J. Crowell, and A. C. Browne. 2006. Exploring lexical forms: First-generation consumer health vocabularies. AMIA Annual Symposium Proceedings (2006), 1155--1155.

Cited By

Ruiz RVelásquez J(2023)Inteligencia artificial al servicio de la salud del futuroRevista Médica Clínica Las Condes10.1016/j.rmclc.2022.12.00134:1(84-91)Online publication date: Jan-2023
https://doi.org/10.1016/j.rmclc.2022.12.001
Sharma ASingh PNikashina PGavrilenko VTselykh ABozhenyuk A(2023)AI and GNN Model for Predictive Analytics on Patient Data and Its Usefulness in Digital Healthcare TechnologiesIoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges10.1007/978-3-031-35783-1_19(331-345)Online publication date: 24-Aug-2023
https://doi.org/10.1007/978-3-031-35783-1_19
Zhu YVenugopalan JZhang ZChanani NMaher KWang M(2022)Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care UnitFrontiers in Artificial Intelligence10.3389/frai.2022.6409265Online publication date: 11-Apr-2022
https://doi.org/10.3389/frai.2022.640926
Show More Cited By

Index Terms

Using Health-Consumer-Contributed Data to Detect Adverse Drug Reactions by Association Mining with Temporal Analysis

Recommendations

Social media mining for drug safety signal detection
SHB '12: Proceedings of the 2012 international workshop on Smart health and wellbeing

Adverse 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 ...
Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media

Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and ...
Identifying Unknown Adverse Drug Reaction Signal Pairs in Postmarketing Surveillance Using an Active Multi-agent System Approach
CITWORKSHOPS '08: Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is passive and suffers from ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 4

Regular Papers and Special Section on Intelligent Healthcare Informatics

August 2015

419 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2801030

Editor:
Yu Zheng
Microsoft Research, China

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2015

Accepted: 01 September 2014

Revised: 01 September 2014

Received: 01 October 2013

Published in TIST Volume 6, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
525
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)4

Reflects downloads up to 10 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ruiz RVelásquez J(2023)Inteligencia artificial al servicio de la salud del futuroRevista Médica Clínica Las Condes10.1016/j.rmclc.2022.12.00134:1(84-91)Online publication date: Jan-2023
https://doi.org/10.1016/j.rmclc.2022.12.001
Sharma ASingh PNikashina PGavrilenko VTselykh ABozhenyuk A(2023)AI and GNN Model for Predictive Analytics on Patient Data and Its Usefulness in Digital Healthcare TechnologiesIoT, Big Data and AI for Improving Quality of Everyday Life: Present and Future Challenges10.1007/978-3-031-35783-1_19(331-345)Online publication date: 24-Aug-2023
https://doi.org/10.1007/978-3-031-35783-1_19
Zhu YVenugopalan JZhang ZChanani NMaher KWang M(2022)Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care UnitFrontiers in Artificial Intelligence10.3389/frai.2022.6409265Online publication date: 11-Apr-2022
https://doi.org/10.3389/frai.2022.640926
Kayesh HIslam MWang JOhira RWang Z(2022)SCANNeurocomputing10.1016/j.neucom.2022.01.019479:C(60-74)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.01.019
Yang C(2022)Explainable Artificial Intelligence for Predictive Modeling in HealthcareJournal of Healthcare Informatics Research10.1007/s41666-022-00114-16:2(228-239)Online publication date: 11-Feb-2022
https://doi.org/10.1007/s41666-022-00114-1
Ruiz RVelásquez J(2022)Artificial Intelligence for the Future of MedicineArtificial Intelligence and Machine Learning for Healthcare10.1007/978-3-031-11170-9_1(1-28)Online publication date: 30-Sep-2022
https://doi.org/10.1007/978-3-031-11170-9_1
Lee JLee YKim DLee HYang BKim M(2021)The Use of Social Media in Detecting Drug Safety–Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping ReviewJMIR Public Health and Surveillance10.2196/301377:6(e30137)Online publication date: 28-Jun-2021
https://doi.org/10.2196/30137
Koss JRheinlaender ATruebel HBohnet-Joschko S(2021)Social media mining in drug development—Fundamentals and use casesDrug Discovery Today10.1016/j.drudis.2021.08.01226:12(2871-2880)Online publication date: Dec-2021
https://doi.org/10.1016/j.drudis.2021.08.012
Wang XZhao KZhou XStreet N(2020)Predicting User Posting Activities in Online Health Communities with Deep LearningACM Transactions on Management Information Systems10.1145/338378011:3(1-15)Online publication date: 21-Jul-2020
https://dl.acm.org/doi/10.1145/3383780
Kim HLiang OYang C(2020)Detecting Potential Adverse Drug Reactions of Preschool ADHD Treatment Using Health Consumer-Generated Content2020 IEEE International Conference on Healthcare Informatics (ICHI)10.1109/ICHI48887.2020.9374395(1-6)Online publication date: Nov-2020
https://doi.org/10.1109/ICHI48887.2020.9374395
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents