IDEAS home Printed from https://ideas.repec.org/a/spr/drugsa/v45y2022i5d10.1007_s40264-022-01155-6.html
   My bibliography  Save this article

Machine Learning in Causal Inference: Application in Pharmacovigilance

Author

Listed:
  • Yiqing Zhao

    (Northwestern University)

  • Yue Yu

    (Mayo Clinic)

  • Hanyin Wang

    (Northwestern University)

  • Yikuan Li

    (Northwestern University)

  • Yu Deng

    (Northwestern University)

  • Guoqian Jiang

    (Mayo Clinic)

  • Yuan Luo

    (Northwestern University)

Abstract

Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.

Suggested Citation

  • Yiqing Zhao & Yue Yu & Hanyin Wang & Yikuan Li & Yu Deng & Guoqian Jiang & Yuan Luo, 2022. "Machine Learning in Causal Inference: Application in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 459-476, May.
  • Handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01155-6
    DOI: 10.1007/s40264-022-01155-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40264-022-01155-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40264-022-01155-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Christopher McMaster & David Liew & Claire Keith & Parnaz Aminian & Albert Frauman, 2019. "Correction to: A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding," Drug Safety, Springer, vol. 42(6), pages 807-807, June.
    2. Shaun Comfort & Sujan Perera & Zoe Hudson & Darren Dorrell & Shawman Meireis & Meenakshi Nagarajan & Cartic Ramakrishnan & Jennifer Fine, 2018. "Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media," Drug Safety, Springer, vol. 41(6), pages 579-590, June.
    3. Shaun Comfort & Darren Dorrell & Shawman Meireis & Jennifer Fine, 2018. "MOdified NARanjo Causality Scale for ICSRs (MONARCSi): A Decision Support Tool for Safety Scientists," Drug Safety, Springer, vol. 41(11), pages 1073-1085, November.
    4. Julia Spoendlin & J. Bradley Layton & Mallika Mundkur & Christian Meier & Susan S. Jick & Christoph R. Meier, 2016. "The Risk of Achilles or Biceps Tendon Rupture in New Statin Users: A Propensity Score-Matched Sequential Cohort Study," Drug Safety, Springer, vol. 39(12), pages 1229-1237, December.
    5. Christopher McMaster & David Liew & Claire Keith & Parnaz Aminian & Albert Frauman, 2019. "A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding," Drug Safety, Springer, vol. 42(6), pages 721-725, June.
    6. Ed Whalen & Manfred Hauben & Andrew Bate, 2018. "Time Series Disturbance Detection for Hypothesis-Free Signal Detection in Longitudinal Observational Databases," Drug Safety, Springer, vol. 41(6), pages 565-577, June.
    7. Timothé Ménard & Björn Koneswarakantha & Donato Rolo & Yves Barmaz & Leszek Popko & Rich Bowling, 2020. "Follow-Up on the Use of Machine Learning in Clinical Quality Assurance: Can We Detect Adverse Event Under-Reporting in Oncology Trials?," Drug Safety, Springer, vol. 43(3), pages 295-296, March.
    8. Carrie E. Pierce & Khaled Bouri & Carol Pamer & Scott Proestel & Harold W. Rodriguez & Hoa Le & Clark C. Freifeld & John S. Brownstein & Mark Walderhaug & I. Ralph Edwards & Nabarun Dasgupta, 2017. "Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts," Drug Safety, Springer, vol. 40(4), pages 317-331, April.
    9. Yuan Luo & William K. Thompson & Timothy M. Herr & Zexian Zeng & Mark A. Berendsen & Siddhartha R. Jonnalagadda & Matthew B. Carson & Justin Starren, 2017. "Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review," Drug Safety, Springer, vol. 40(11), pages 1075-1089, November.
    10. Juan M. Banda & Alison Callahan & Rainer Winnenburg & Howard R. Strasberg & Aurel Cami & Ben Y. Reis & Santiago Vilar & George Hripcsak & Michel Dumontier & Nigam Haresh Shah, 2016. "Feasibility of Prioritizing Drug–Drug-Event Associations Found in Electronic Health Records," Drug Safety, Springer, vol. 39(1), pages 45-57, January.
    11. Rave Harpaz & Alison Callahan & Suzanne Tamang & Yen Low & David Odgers & Sam Finlayson & Kenneth Jung & Paea LePendu & Nigam Shah, 2014. "Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art," Drug Safety, Springer, vol. 37(10), pages 777-790, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lucie M. Gattepaille & Sara Hedfors Vidlin & Tomas Bergvall & Carrie E. Pierce & Johan Ellenius, 2020. "Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project," Drug Safety, Springer, vol. 43(8), pages 797-808, August.
    2. Yuan Luo & William K. Thompson & Timothy M. Herr & Zexian Zeng & Mark A. Berendsen & Siddhartha R. Jonnalagadda & Matthew B. Carson & Justin Starren, 2017. "Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review," Drug Safety, Springer, vol. 40(11), pages 1075-1089, November.
    3. Heba Edrees & Wenyu Song & Ania Syrowatka & Aurélien Simona & Mary G. Amato & David W. Bates, 2022. "Intelligent Telehealth in Pharmacovigilance: A Future Perspective," Drug Safety, Springer, vol. 45(5), pages 449-458, May.
    4. Andrew Bate & Steve F. Hobbiger, 2021. "Artificial Intelligence, Real-World Automation and the Safety of Medicines," Drug Safety, Springer, vol. 44(2), pages 125-132, February.
    5. Tavpritesh Sethi & Nigam H. Shah, 2017. "Pharmacovigilance Using Textual Data: The Need to Go Deeper and Wider into the Con(text)," Drug Safety, Springer, vol. 40(11), pages 1047-1048, November.
    6. Gianluca Trifirò & Janet Sultana & Andrew Bate, 2018. "From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources," Drug Safety, Springer, vol. 41(2), pages 143-149, February.
    7. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    8. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    9. Susan Colilla & Elad Yom Tov & Ling Zhang & Marie-Laure Kurzinger & Stephanie Tcherny-Lessenot & Catherine Penfornis & Shang Jen & Danny S. Gonzalez & Patrick Caubel & Susan Welsh & Juhaeri Juhaeri, 2017. "Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS," Drug Safety, Springer, vol. 40(5), pages 399-408, May.
    10. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.
    11. Eyal Eckhaus & Zachary Sheaffer, 2018. "Managerial hubris detection: the case of Enron," Risk Management, Palgrave Macmillan, vol. 20(4), pages 304-325, November.
    12. Camille Goyer & Genaro Castillon & Yola Moride, 2022. "Implementation of Interventions and Policies on Opioids and Awareness of Opioid-Related Harms in Canada: A Multistage Mixed Methods Descriptive Study," IJERPH, MDPI, vol. 19(9), pages 1-12, April.
    13. Odile Sauzet & Julia Dyck & Victoria Cornelius, 2024. "Optimal Significance Levels and Sample Sizes for Signal Detection Methods Based on Non-constant Hazards," Drug Safety, Springer, vol. 47(11), pages 1149-1156, November.
    14. Bissan Audeh & Florelle Bellet & Marie-Noëlle Beyens & Agnès Lillo-Le Louët & Cédric Bousquet, 2020. "Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project," Drug Safety, Springer, vol. 43(9), pages 835-851, September.
    15. Yves Barmaz & Timothé Ménard, 2021. "Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials," Drug Safety, Springer, vol. 44(9), pages 949-955, September.
    16. Juergen Dietrich & Lucie M. Gattepaille & Britta Anne Grum & Letitia Jiri & Magnus Lerch & Daniele Sartori & Antoni Wisniewski, 2020. "Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR," Drug Safety, Springer, vol. 43(5), pages 467-478, May.
    17. Na Zhang & Ping Yu & Yupeng Li & Wei Gao, 2022. "Research on the Evolution of Consumers’ Purchase Intention Based on Online Reviews and Opinion Dynamics," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    18. Ying Li & Antonio Jimeno Yepes & Cao Xiao, 2020. "Combining Social Media and FDA Adverse Event Reporting System to Detect Adverse Drug Reactions," Drug Safety, Springer, vol. 43(9), pages 893-903, September.
    19. Galit Klein & Eyal Eckhaus, 2017. "Sensemaking and sensegiving as predicting organizational crisis," Risk Management, Palgrave Macmillan, vol. 19(3), pages 225-244, August.
    20. Apostolos G. Katsafados & Dimitris Anastasiou, 2024. "Short-term prediction of bank deposit flows: do textual features matter?," Annals of Operations Research, Springer, vol. 338(2), pages 947-972, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:drugsa:v:45:y:2022:i:5:d:10.1007_s40264-022-01155-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/40264 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.