Clinical Psychopharmacology and Neuroscience Papers in Press available online.

Machine-Learning for prescription patterns: Random Forest in the prediction of dose and number of antipsychotics prescribed to people with Schizophrenia
Mattia Marchi 1,*, Giacomo Galli1, Gianluca Fiore1, Andrew Mackinnon2, Giorgio Mattei1, Fabrizio Starace3, Gian Maria Galeazzi1,3,4
1Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Italy, 2Black Dog Institute, University of New South Wales, Sydney, NSW, Australia, 3Department of Mental Health and Drug Abuse, Azienda Unità Sanitaria Locale (AUSL) Modena, Italy, 4Dipartimento di Salute Mentale e Dipendenze Patologiche, AUSL-IRCSS di Reggio Emilia, Via Amendola 2, 42122 Reggio Emilia, Italy
Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms.
Methods: In a cross-sectional design, a sample of community mental health service users (SUs; N=368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square [OLS] regression and ML algorithms (i.e., Random Forest [RF], Supported Vector Machine [SVM], K-nearest Neighborhood [KNN], and Naïve Bayes [NB]) were used to estimate the predictors of total antipsychotic dosage and prescription of antipsychotic polytherapy (APP).
Results: The strongest predictor of the total dose was APP. The number of Community Mental Health Centers (CMHC) contacts was the most important predictor of APP and, with APP omitted, of dosage. Treatment with anticholinergics predicted APP, emphasizing the strong correlation between APP and higher antipsychotic dose. RF performed better than OLS regression and the other ML algorithms in predicting both antipsychotic dose (Root Square Mean Error [RMSE]=0.70, R2=0.31) and APP (Area Under the Receiving Operator Curve [AUROC]=0.66, true positive rate=0.41, and true negative rate=0.78).
Conclusion: APP is associated with the prescription of higher total doses of antipsychotics. Frequent attenders at CMHCs, and SUs recently hospitalized are often treated with APP and higher doses of antipsychotics. Future prospective studies incorporating standardized clinical assessments for both psychopathological severity and treatment efficacy are needed to confirm these findings.
Accepted Manuscript [Submitted on 2021-04-21, Accepted on 2021-09-23]