Deep neural network architectures for forecasting analgesic response.
Deep neural network architectures for forecasting analgesic response.
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DOI：
10.1109/embc.2016.7591352
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发表时间：
2016-08
期刊：
影响因子：
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通讯作者：
Rashidi P
中科院分区：
文献类型：
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作者：
Nickerson P;Tighe P;Shickel B;Rashidi P
对处方止痛药的反应因人而异，选择正确的药物/剂量往往涉及漫长的反复试验和错误的过程。此外，很大一部分患者在住院治疗急性术后疼痛期间发生不良事件，如术后尿潴留（POUR）。为了更好地预测镇痛反应，我们将传统的机器学习方法与现代神经网络架构进行了比较，以评估它们在预测术后疼痛和镇痛药使用的时间模式以及预测POUR风险方面的有效性。我们的研究结果表明，更简单的机器学习方法可能会提供上级的结果;然而，所有这些技术都可能在开发更智能的术后疼痛管理策略方面发挥有前途的作用。
Response to prescribed analgesic drugs varies between individuals, and choosing the right drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a significant portion of patients experience adverse events such as post-operative urinary retention (POUR) during inpatient management of acute postoperative pain. To better forecast analgesic responses, we compared conventional machine learning methods with modern neural network architectures to gauge their effectiveness at forecasting temporal patterns of postoperative pain and analgesic use, as well as predicting the risk of POUR. Our results indicate that simpler machine learning approaches might offer superior results; however, all of these techniques may play a promising role for developing smarter post-operative pain management strategies.