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Journal of Medical Signals and Sensors، جلد ۱۵، شماره ۱، صفحات ۱۰-۴۱۰۳
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عنوان انگلیسی Isfahan Artificial Intelligence Event 2023: Drug Demand Forecasting
چکیده انگلیسی مقاله Abstract Background:  The pharmaceutical industry has seen increased drug production by different manufacturers. Failure to recognize future needs has caused improper production and distribution of drugs throughout the supply chain of this industry. Forecasting demand is one of the basic requirements to overcome these challenges. Forecasting the demand helps the drug to be well estimated and produced at a certain time. Methods:  Artificial intelligence (AI) technologies are suitable methods for forecasting demand. The more accurate this forecast is the better it will be to decide on the management of drug production and distribution. Isfahan AI competitions-2023 have organized a challenge to provide models for accurately predicting drug demand. In this article, we introduce this challenge and describe the proposed approaches that led to the most successful results. Results:  A dataset of drug sales was collected in 12 pharmacies of Hamadan University of Medical Sciences. This dataset contains 8 features, including sales amount and date of purchase. Competitors compete based on this dataset to accurately forecast the volume of demand. The purpose of this challenge is to provide a model with a minimum error rate while addressing some qualitative scientific metrics. Conclusions:  In this competition, methods based on AI were investigated. The results showed that machine learning methods are particularly useful in drug demand forecasting. Furthermore, changing the dimensions of the data features by adding the geographic features helps increase the accuracy of models.
کلیدواژه‌های انگلیسی مقاله Drug demand forecasting,Isfahan artificial intelligence competitions,Supply chain management
نویسندگان مقاله | Meysam Jahani
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Zahra Zojaji
Department of Information Technology, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| AhmadReza Montazerolghaem
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran


| Maziar Palhang
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Reza Ramezani
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Ahmadreza Golkarnoor
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Alireza Akhavan Safaei
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Hossein Bahak
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Pegah Saboori
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Behnam Soufi Halaj
Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Ahmad R. Naghsh-Nilchi
Artificial Intelligence Department, Faculty of Computer Engineering, Shiraz University, Shiraz, Iran


| Fatemeh Mohamadpoor
Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran


| Saeid Jafarizadeh


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/738
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Methodology Articles
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