Potential Variables in Pharmaceutical Drug Prediction Research with Machine Learning Approach: A Literature Review

Abstract

As a downstream component of the drug supply chain, pharmaceutical installations often face uncertainty in drug demand. Predicting pharmaceutical drugs using a machine learning approach enables the development of new variables that can enhance the performance of medicine prediction. Amidst limited data and a choice of prediction algorithms, the accuracy of variable selection is significant for drug prediction performance. This study remaps the scope of variables from previous studies related to drug demand prediction and machine learning performance to develop further significant variables. Investigating research literature on significant variables in drug demand prediction with machine learning models published in 2020-2024. The systematic literature methodology uses the Kitchenham method. Mapping problems, discussion areas, and data availability result in ten categories of issue areas, each with its respective data needs and algorithm choices. A qualitative exploration of issue areas identifies potential variables for pharmaceutical drug prediction, including drug consumption, epidemiology, drug management, supply chain-patient domicile, and pharmacotherapy. Mapping potential variables facilitates the availability and integration of data relevant to local or regional characteristics, enabling further research on the characteristics of data and algorithm choices.