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

住院婴儿的疼痛得不到控制会导致严重的长期并发症。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，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）采用相同的测量方法，没有尝试优化它们以改善疼痛评估（例如加权）

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.