美国麻省理工学院Kreshnik Hoti和Mingmin Zhao研究组合作使用人工智能评估药物自我管理（MSA）。该项研究成果发表在2021年3月18日出版的《自然-医学》杂志上。

他们提供了一种非接触式且无干扰的人工智能（AI）框架，该框架可通过分析患者家中的无线信号来检测和监视MSA错误，而无需进行物理接触。该系统是通过观察志愿者的自我管理而开发的，并通过将其预测与人类注释进行比较来进行评估。

这项研究的结果表明，他们的方法可以自动检测患者何时使用吸入器（曲线下面积（AUC）= 0.992）或胰岛素笔（AUC = 0.967），并评估患者是否遵循使用这些设备的适当步骤（AUC = 0.952）。这项工作显示了利用基于AI的解决方案对于患者和卫生专业人员以最小的开销提高药物安全性的潜力。

研究人员表示，MSA的错误导致治疗依从性差、住院增加和医疗费用增加。当药物输送涉及诸如吸入器或胰岛素笔之类的设备时，这些错误特别常见。

附：英文原文

Title: Assessment of medication self-administration using artificial intelligence

Author: Mingmin Zhao, Kreshnik Hoti, Hao Wang, Aniruddh Raghu, Dina Katabi

Issue&Volume: 2021-03-18

Abstract: Errors in medication self-administration (MSA) lead to poor treatment adherence, increased hospitalizations and higher healthcare costs. These errors are particularly common when medication delivery involves devices such as inhalers or insulin pens. We present a contactless and unobtrusive artificial intelligence (AI) framework that can detect and monitor MSA errors by analyzing the wireless signals in the patient’s home, without the need for physical contact. The system was developed by observing self-administration conducted by volunteers and evaluated by comparing its prediction with human annotations. Findings from this study demonstrate that our approach can automatically detect when patients use their inhalers (area under the curve (AUC)=0.992) or insulin pens (AUC=0.967), and assess whether patients follow the appropriate steps for using these devices (AUC=0.952). The work shows the potential of leveraging AI-based solutions to improve medication safety with minimal overhead for patients and health professionals.

DOI: 10.1038/s41591-021-01273-1

Source: https://www.nature.com/articles/s41591-021-01273-1