Predicting Drug Sale Quantity Using Machine Learning

Medication is one of the essential parts of a patient’s treatment. Therefore, it is important to have good medication storage administration in order to have effective medication storage. This study aimed to find a proper model used for the prediction of medication purchase amount by using machine learning to analyze medication purchasing amounts in the form of time series. In this research, the first 10 medicines in AV group were chosen. Then, Multilayer Perceptron (MLP), Long Shot-Term Memory (LSTM), and 1D Convolutional neural network with LSTM models were used together with Rolling Windows which were used to predict the purchase amount of each model. The periods of prediction were at 1 month, 3 months, and 6 months. The efficacy of each model was compared using their errors. CNN-LSTM model produces the better forecasting results. The result also shows that 1-month forecasting period is suitable for medicines that specific to disease. The 3-month forecasting period is suitable for commonly used medicines. The 6-month forecasting period is suitable for the medicines for chronic diseases.

[1]  Artem A. Koshkarov,et al.  Automation of planning of medical-economic drug prescription control , 2016, 2016 IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS).

[2]  S Aparna,et al.  Long Short Term Memory and Rolling Window Technique for Modeling Power Demand Prediction , 2018, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS).

[3]  D. Kritchanchai,et al.  Supply chain management in health sector in Thailand: a case study , 2010 .

[4]  Katsutoshi Yada,et al.  A Deep Learning Approach for the Prediction of Retail Store Sales , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[5]  D. Kritchanchai.  A Framework for Healthcare Supply Chain Improvement in Thailand , 2014 .

[6]  Vicente Julián,et al.  Rainfall Prediction: A Deep Learning Approach , 2016, HAIS.

[7]  K. Abhishek,et al.  A rainfall prediction model using artificial neural network , 2012, 2012 IEEE Control and System Graduate Research Colloquium.

[8]  D. Kritchanchai,et al.  Developing Inventory Management in Hospital , 2015 .

[9]  F. Vaz,et al.  A Study of Drug Expenditure at a Tertiary Care Hospital , 2008 .

[10]  Mohamed Jmaiel,et al.  Recursive and Rolling Windows for Medical Time Series Forecasting: A Comparative Study , 2016, 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES).

[11]  Kyoto Yono,et al.  Prediction of regional goods demand incorporating the effect of weather , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[12]  Enda Barrett,et al.  CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models , 2017, 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST).