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Deep neural network architectures for forecasting analgesic response.

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      Abstract

      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.

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      Author and article information

      Journal
      Conf Proc IEEE Eng Med Biol Soc
      Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
      Institute of Electrical and Electronics Engineers (IEEE)
      1557-170X
      1557-170X
      August 2016
      : 2016
      28268935
      10.1109/EMBC.2016.7591352
      5445646
      NIHMS862168

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