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

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.