Kiryu Yoshihiro
M&B Collaboration Medical Corporation Hokuetsu Hospital.
Yakugaku Zasshi. 2021;141(2):179-185. doi: 10.1248/yakushi.20-00196-4.
Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse effects that cannot be predicted by conventional methods. We have developed an adverse drug reactions analysis system that uses machine learning and data from the Japanese Adverse Drug Event Report (JADER) database. The system was developed using the C# programming language and incorporates the open source machine learning library Accord.Net. Potential analytical capabilities of the system include discovering unknown drug adverse effects and evaluating drug-induced adverse events in pharmaceutical management. However, to apply the system to pharmaceutical management, it is important to examine the characteristics and suitability of the level of AI used in the system and to select statistical methods or machine learning when appropriate. If these points are addressed, there is potential for pharmaceutical management to be individualized and optimized in the clinical setting by using the developed system to analyze big data. The system also has the potential to allow individual healthcare facilities such as hospitals and pharmacies to contribute to drug repositioning, including the discovery of new efficacies, interactions, and drug adverse events.
近年来，利用人工智能（AI）的产业改革取得了显著进展。AI在医学信息领域大数据分析中的应用也在不断推进，有望用于发现传统方法无法预测的药物不良反应。我们开发了一种药物不良反应分析系统，该系统使用机器学习和来自日本药品不良反应报告（JADER）数据库的数据。该系统使用C#编程语言开发，并集成了开源机器学习库Accord.Net。该系统的潜在分析能力包括发现未知的药物不良反应以及在药物管理中评估药物引起的不良事件。然而，要将该系统应用于药物管理，重要的是要检查系统中使用的AI水平的特征和适用性，并在适当的时候选择统计方法或机器学习。如果解决了这些问题，那么通过使用开发的系统分析大数据，在临床环境中药物管理就有可能实现个性化和优化。该系统还有潜力使医院和药房等个体医疗机构为药物重新定位做出贡献，包括发现新的疗效、相互作用和药物不良反应。
