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Predicting the Physician's Specialty Using a Medical Prescription Database Publisher Pubmed



Akhlaghi M1 ; Tabesh H2 ; Mahaki B3 ; Malekpour MR4 ; Ghasemi E4 ; Mansourian M5
Authors
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Authors Affiliations
  1. 1. Department of Biostatistics and Epidemiology, School of Health, Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
  3. 3. Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
  4. 4. Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Epidemiology and Biostatistics Department, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Computational and Mathematical Methods in Medicine Published:2022


Abstract

Purpose. The present study is aimed at predicting the physician's specialty based on the most frequent two medications prescribed simultaneously. The results of this study could be utilized in the imputation of the missing data in similar databases. Patients and Methods. The research is done through the KAy-means for MIxed LArge datasets (KAMILA) clustering and random forest (RF) model. The data used in the study were retrieved from outpatients' prescriptions in the second populous province of Iran (Khorasan Razavi) from April 2015 to March 2017. Results. The main findings of the study represent the importance of each combination in predicting the specialty. The final results showed that the combination of amoxicillin-metronidazole has the highest importance in making an accurate prediction. The findings are provided in a user-friendly R-shiny web application, which can be applied to any medical prescription database. Conclusion. Nowadays, a huge amount of data is produced in the field of medical prescriptions, which a significant section of that is missing in the specialty. Thus, imputing the missing variables can lead to valuable results for planning a medication with higher quality, improving healthcare quality, and decreasing expenses. © 2022 Mahboube Akhlaghi et al.