Jahani Meysam, Zojaji Zahra, Montazerolghaem AhmadReza, Palhang Maziar, Ramezani Reza, Golkarnoor Ahmadreza, Safaei Alireza Akhavan, Bahak Hossein, Saboori Pegah, Halaj Behnam Soufi, Naghsh-Nilchi Ahmad R, Mohamadpoor Fatemeh, Jafarizadeh Saeid
Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
Department of Information Technology, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
J Med Signals Sens. 2024 Jan 23;15:2. doi: 10.4103/jmss.jmss_59_24. eCollection 2025.
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.
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.
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.
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.
制药行业中不同制造商的药品产量有所增加。未能识别未来需求导致该行业供应链中药品生产和分销不当。预测需求是克服这些挑战的基本要求之一。预测需求有助于在特定时间对药品进行准确估计和生产。
人工智能（AI）技术是预测需求的合适方法。这种预测越准确，就越有利于对药品生产和分销管理做出决策。2023年伊斯法罕人工智能竞赛组织了一项挑战，以提供准确预测药品需求的模型。在本文中，我们介绍了这项挑战，并描述了取得最成功结果的建议方法。
在哈马丹医科大学的12家药店收集了药品销售数据集。该数据集包含8个特征，包括销售额和购买日期。参赛者基于此数据集进行竞争，以准确预测需求量。这项挑战的目的是在满足一些定性科学指标的同时，提供一个错误率最低的模型。
在本次竞赛中，对基于人工智能的方法进行了研究。结果表明，机器学习方法在药品需求预测中特别有用。此外，通过添加地理特征来改变数据特征的维度有助于提高模型的准确性。
