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

住院婴儿的疼痛若得不到控制，会导致严重的长期并发症。然而，为了控制疼痛，必须进行准确的婴儿疼痛评估。婴儿无法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重大问题，因为：1）不同的疼痛指标（心率、氧气水平、面部活动）的处理方式相同，没有尝试优化它们以改善疼痛评估（例如，对它们进行不同的加权）。 2) 没有一种“黄金标准”工具能够区分侵入性操作（如高跟枪）引起的痛苦和非侵入性操作（如弄脏的尿布）引起的痛苦。 3) 护理人员可能存在影响其疼痛判断的偏见。我们的国际知识用户团队和健康/自然科学/工程/社会科学研究人员齐心协力构建了一种机器学习算法，该算法将学习如何区分侵入性和非侵入性痛苦。将在大约相隔 1 周的 2 次痛苦手术（足跟采血术）中对 300 名早产儿及其母亲进行跟踪。第一次痛苦手术期间的疼痛指标（面部表情、心率、脑电活动、氧气水平）将用于训练算法来区分第二次手术期间不同类型的痛苦。我们的团队还将在疼痛手术前更长时间地测量婴儿心率和氧气水平模式，以便更好地了解疼痛手术期间和之后婴儿的反应。此外，这将是第一次在临床工具中考虑大脑活动。我们断言这提高了我们区分侵入性和非侵入性痛苦的能力。疼痛的复杂性需要机器学习解决方案能够对疼痛期间的大脑、行为和生理的个体模式进行建模。从本质上讲，这最终将允许早产儿为自己“自我报告”疼痛。

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. weightingthem differently). 2) None of the "gold standard" tools can discriminate distress from invasive procedures (like aheel 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/socialscience 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.