Predicting polypharmacy in half a million adults in the Iranian population: comparison of machine learning algorithms.

Seyedtabib, Maryam; Kamyari, Naser

Seyedtabib, Maryam; Kamyari, Naser.

Affiliation

Seyedtabib M; Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Kamyari N; Department of Biostatistics and Epidemiology, School of Health, Abadan University of Medical Sciences, Abadan, Iran. n.kamyari@abadanums.ac.ir.

BMC Med Inform Decis Mak ; 23(1): 84, 2023 05 05.

Article in En | MEDLINE | ID: mdl-37147615

Subject(s)

Algorithms; Polypharmacy; Humans; Adult; Female; Middle Aged; Male; Iran/epidemiology; Cross-Sectional Studies; Machine Learning

Key words

Artificial intelligence; Iranian; Machine learning; Polypharmacy; Random Forest

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Full text: 1 Database: MEDLINE Main subject: Algorithms / Polypharmacy Type of study: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limits: Adult / Female / Humans / Male / Middle aged Country/Region as subject: Asia Language: En Year: 2023 Type: Article

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