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

住院婴儿无节制的疼痛有严重的长期并发症。然而，要管理疼痛，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，目前卫生专业人员使用的婴儿疼痛评估工具存在主要问题，因为：1)不同的疼痛指标(心率、氧气水平、面部活动)被同等对待，没有尝试优化它们以改善疼痛评估(例如加权) 它们是不同的)。2)没有一种“黄金标准”工具能够区分痛苦和侵入性程序(如 由于非侵入性操作(如弄脏的尿布)，可保护脚后跟和脚跟)。3)照顾者的偏见可能会影响他们对疼痛的判断。我们的知识用户和健康/自然科学/工程/社会的国际团队 科学研究人员联合起来构建了一种机器学习算法，该算法将学习如何区分侵袭性和非侵袭性痛苦。300名早产儿和他们的母亲的样本将在两个痛苦的程序(脚后跟刺刀)中被跟踪，大约相隔一周。疼痛指标(面部表情、心率、脑电活动、氧气水平) 在第一个痛苦的过程中，将使用训练算法来区分第二个过程中的不同类型的痛苦。我们的团队还将在痛苦的手术之前更长时间地测量婴儿的心率和氧气水平模式，以更好地了解婴儿在痛苦手术过程中和之后的反应。此外，这将是第一次考虑将大脑活动用于临床工具。我们断言，这提高了我们区分侵袭性和非侵袭性痛苦的能力。疼痛的复杂性需要一种机器学习解决方案，该解决方案能够对疼痛过程中大脑、行为和生理的个人模式进行建模。从本质上讲，这最终将允许早产儿 “自我报告”自己的痛苦。

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.