African Center of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, P.O. Box 3900, Kigali, Rwanda.
International Centre of Theoretical Physics, Strada Costiera, 11, Trieste I-34151, Italy.
J Healthc Eng. 2021 May 3;2021:9990552. doi: 10.1155/2021/9990552. eCollection 2021.
Temperature control is the key element during medicine storage. Pharmacies sell some medical products which are kept in fridges. The opening and closing of the fridge while taking some medicine makes the outside hot air enter the fridge, which will increase the inner fridge temperature. When the frequency of opening and closing of the fridge is increased, the temperature may go beyond the allowed storage temperature range. In this paper, we are proposing a model with the help of machine learning that will be used in multiple chambers fridges to keep indicating the time remaining for the inner temperature to go beyond the allowed range, and if the time is short, the system will propose to the pharmacist not to open that particular room and proposes a room that has enough time slots (time to reach the upper limit temperature). By using training data got from a thermoelectric cooler-based fridge, we constructed a multiple linear regression model that can predict the required time for a given room to reach the cut-off temperature in case that room is opened. The built model was evaluated using the coefficient of determination and is found to be 77%, and then it can be used to develop a multiple room smart fridge for efficiently storing highly sensitive medical products.
温度控制是药品储存的关键要素。药店销售一些保存在冰箱里的医疗产品。在取药时，冰箱的开关会使外界热空气进入冰箱，从而导致冰箱内部温度升高。当冰箱的开关频率增加时，温度可能会超过允许的储存温度范围。在本文中，我们提出了一个基于机器学习的模型，该模型将用于多室冰箱，以持续指示内部温度超过允许范围的剩余时间，如果时间较短，系统将建议药剂师不要打开那个特定的房间，并建议一个有足够时间（达到上限温度）的房间。我们使用基于热电冷却器的冰箱获得的训练数据构建了一个多元线性回归模型，该模型可以预测给定房间在打开时达到截止温度所需的时间。所构建的模型使用确定系数 进行评估，发现为 77%，然后可以用于开发一个多室智能冰箱，以有效地储存高度敏感的医疗产品。
