Machine Learning for Concussion Recovery Prognosis: A Novel Tool to Empower Proactive Physician Treatments.
Machine Learning for Concussion Recovery Prognosis: A Novel Tool to Empower Proactive Physician Treatments.
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用于脑震荡恢复预测的机器学习：一种支持医生积极治疗的新工具。
DOI：
10.1542/peds.144.2_meetingabstract.198
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发表时间：
2019
期刊：
影响因子：
8
通讯作者：
Kelly Cohen
中科院分区：
文献类型：
--
作者：
Gregory Walker;A. Kiefer;Nathaniel Richards;E. Klug;Christy Reed;Paula L. Silva;Kelly Cohen
背景：目前儿童脑震荡的治疗是反应性的，而不是主动性的。预测儿童脑震荡患者的康复时间是改变这种情况并支持精确医学治疗计划发展的关键。将既往脑震荡、情绪障碍并存和偏头痛障碍与脑震荡长期恢复的风险增加联系起来的研究有限。然而，我们仍然难以准确预测脑震荡患者的康复时间。机器学习是一种强大的分析方法，可以作为医生的决策支持工具。此…
Background: Current treatment of pediatric concussions is reactive rather than proactive. Predicting time-to-recovery in pediatric concussion patients is key to change this scenario and support development of precision medicine treatment plan. There is limited research linking prior concussion, coexistence of mood disorder, and migraine disorder to an increased risk for prolonged recovery from concussion. However, we still struggle to accurately predict time-to-recovery in concussed patients. Machine learning is a powerful analytic method that may be useful as a decision-support tool for physicians. This …