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Frontiers in pharmacology
2020;11 1028.

Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.

Maurizio Sessa, Abdul Rauf Khan, David Liang, Morten Andersen, Murat Kulahci
Author Information
  1. Maurizio Sessa: Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
  2. Abdul Rauf Khan: Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
  3. David Liang: Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
  4. Morten Andersen: Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
  5. Murat Kulahci: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark.
PMID: 32765261 DOI: 10.3389/fphar.2020.01028

Abstract

AIM: To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology.
STUDY ELIGIBILITY CRITERIA: Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.
DATA SOURCES: Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened.
PARTICIPANTS: Studies including humans (real or simulated) exposed to a drug.
RESULTS: In total, 72 original articles and 5 reviews were identified Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models.
CONCLUSIONS: The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology.
SYSTEMATIC REVIEW REGISTRATION: Systematic review registration number in PROSPERO: CRD42019136552.

Keywords

artificial intelligence deep learning machine learning pharmacoepidemiology systematic review

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Systematic Review Journal Article
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