Development of a prescription recommendation system using case-based reasoning

In this thesis, a prescription recommendation system was developed based on past prescriptions in order to reduce the workload of physicians and increase the accuracy of written prescriptions. The case-based reasoning method used in this research is among the technological developments used in real life in different fields. In this study, the prescription recommendation system was developed using case-based reasoning method and research was performed to find out the performance of this system. In order to create a set of data to be used in the study, 7120 anonymous prescription information was collected from 300 volunteers through a website. The success rate of the system was then calculated by comparing the prescriptions (1) with the prescriptions prescribed by ten physicians in different branches, and (2) with the latest prescriptions in the data set by using the nearest neighbors' algorithm. The success rate of the system obtained by comparing the prescriptions of the real-life prescriptions for the patients and the recommended prescriptions is 0.78. Besides, the success rate of the system regarding the comparison of the last prescriptions of the 50 most common diseases in the system's data set and the prescriptions recommended by the system is 0.91. The results of this study indicate that health-care professionals can benefit from the recommendation system developed in this study. In general, with the recommendation system designed in this study, health-care professionals are supported to make faster and more accurate decisions during the prescription writing process.