Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice.

重新启动婴儿疼痛评估：利用机器学习以指数方式改善新生儿重症监护病房的实践。

• 批准号: 538853-2019
• 负责人: PillaiRiddell, Rebecca
• 金额: $ 10.25万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=697786
• 关键词: Rebooting; Infant; Pain; Assessment; Using

Unmanaged pain in hospitalized infants has serious long term complications. However, to manage pain, one must have accurate infant pain assessment. Infants cannot self-report their pain and current infant pain assessment tools used by health professionals have major problems because: 1) Different pain indicators (heart rate, oxygen levels, facial activity) are treated the same inmeasures, with no attempts to optimize them to improve pain assessment (e.g. weighting them differently). 2) None of the "gold standard" tools can discriminate distress from invasive procedures (like a heel lance) from distress due to non-invasive procedures (like a soiled diaper). 3) Caregivers can have biases that impact their pain judgments. Our international team of knowledge users and health/natural science/engineering/social science researchers have come together to build a machine learning algorithm that will learn how to discriminate invasive and non-invasive distress. A sample of 300 preterm infants and their mothers will be followed during 2 painful procedures (heel lance) approximately 1-week apart. Pain indicators (facial grimacing, heart rate, brain electrical activity, oxygen levels) during the first painful procedure will be used to train the algorithm to discriminate between the different types of distress during the second procedure. Our team will also measure the infant heart rate and oxygen level patterns longer before the painful procedure to better contextualize infant responses during and after the painful procedure. Also, this will be the first time brain activity is being considered in a clinical tool. We assert this increases our ability to discriminate between invasive and non-invasive distress. The complexity of pain requires a machine learning solution that is capable of modelling individual patterns of brain, behaviour, and physiology during pain. In essence, this will finally allow preterm infants to 'self-report' pain for themselves.

住院婴儿的疼痛无法控制，有严重的长期并发症。然而，要控制疼痛，必须有准确的婴儿疼痛评估。婴儿不能自我报告他们的疼痛，并且由卫生专业人员使用的当前婴儿疼痛评估工具具有主要问题，因为：1）不同的疼痛指标（心率、氧气水平、面部活动）被相同地处理，没有尝试优化它们以改善疼痛评估（例如，加权 不同的）。2)没有一个“黄金标准”工具可以区分痛苦和侵入性程序（如 脚跟长矛）从痛苦由于非侵入性程序（如弄脏尿布）。3)照顾者可能会有偏见，影响他们的疼痛判断。我们的知识用户和健康/自然科学/工程/社会的国际团队 科学研究人员已经聚集在一起，建立一个机器学习算法，将学习如何区分侵入性和非侵入性的痛苦。将对300名早产儿及其母亲的样本进行2次疼痛手术（足跟穿刺），间隔约1周。疼痛指标（面部表情、心率、脑电活动、氧气水平） 将用于训练算法以在第二过程期间区分不同类型的痛苦。我们的团队还将在疼痛过程之前更长时间测量婴儿心率和氧气水平模式，以更好地了解婴儿在疼痛过程中和之后的反应。此外，这将是第一次在临床工具中考虑大脑活动。我们断言，这增加了我们区分侵入性和非侵入性痛苦的能力。疼痛的复杂性需要一种机器学习解决方案，能够对疼痛期间大脑、行为和生理的个体模式进行建模。从本质上讲，这将最终允许早产儿 为自己的“痛苦”而战。