Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence
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Artificial Intelligence in Pharmacoepidemiology : A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence. / Sessa, Maurizio; Khan, Abdul Rauf; Liang, David; Andersen, Morten; Kulahci, Murat.
In: Frontiers in Pharmacology, Vol. 11, 1028, 2020.Research output: Contribution to journal › Review › Research › peer-review
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TY - JOUR
T1 - Artificial Intelligence in Pharmacoepidemiology
T2 - A Systematic Review. Part 1—Overview of Knowledge Discovery Techniques in Artificial Intelligence
AU - Sessa, Maurizio
AU - Khan, Abdul Rauf
AU - Liang, David
AU - Andersen, Morten
AU - Kulahci, Murat
PY - 2020
Y1 - 2020
N2 - 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 via 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.
AB - 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 via 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.
KW - artificial intelligence
KW - deep learning
KW - machine learning
KW - pharmacoepidemiology
KW - systematic review
U2 - 10.3389/fphar.2020.01028
DO - 10.3389/fphar.2020.01028
M3 - Review
C2 - 32765261
AN - SCOPUS:85088825774
VL - 11
JO - Frontiers in Pharmacology
JF - Frontiers in Pharmacology
SN - 1663-9812
M1 - 1028
ER -
ID: 247689152