Predicting the Outcome of Patient-Provider Communication Sequences using Recurrent Neural Networks and Probabilistic Models
Predicting the Outcome of Patient-Provider Communication Sequences using Recurrent Neural Networks and Probabilistic Models
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发表时间：
2018-05
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通讯作者：
M. Hasan;Alexander Kotov;A. Carcone;Ming Dong;Sylvie Naar
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作者：
M. Hasan;Alexander Kotov;A. Carcone;Ming Dong;Sylvie Naar
分析由分子、生理或心理过程产生的按时间排序的观察序列以对这些过程的结果进行预测的问题出现在临床信息学的许多领域中。在本文中，我们专注于预测临床对话背景下患者与提供者沟通序列的结果。具体来说，我们将基于观察到的编码患者-提供者通信交换序列的动机访谈成功预测（即引发特定类型的患者行为反应）视为序列分类问题。我们针对这个问题提出了两种解决方案，一种基于循环神经网络（RNN），另一种基于马尔可夫链（MC）和隐马尔可夫模型（HMM），并使用用现实生活中的动机访谈中的行为代码注释的通信序列来比较这些解决方案的准确性。我们的实验表明，在预测动机性访谈的成功方面，基于深度学习的方法比基于概率模型的方法要准确得多（当使用欠采样来校正类别不平衡时，RNN、MC 和 HMM 的 F1 分数分别为 0.8677、0.7038 和 0.6067，而使用欠采样校正类别不平衡时，F1 分数分别为 0.8381、0.7775 和 0.7520循环神经网络， MC 和 HMM，分别使用过采样时）。这些结果表明，所提出的方法可用于实时监控临床访谈的进展，并更有效地识别有效的提供者沟通策略，这反过来又可以显着减少开发行为干预所需的工作并提高其有效性。
The problem of analyzing temporally ordered sequences of observations generated by molecular, physiological or psychological processes to make predictions about the outcome of these processes arises in many domains of clinical informatics. In this paper, we focus on predicting the outcome of patient-provider communication sequences in the context of the clinical dialog. Specifically, we consider prediction of the motivational interview success (i.e. eliciting a particular type of patient behavioral response) based on an observed sequence of coded patient-provider communication exchanges as a sequence classification problem. We propose two solutions to this problem, one that is based on Recurrent Neural Networks (RNNs) and another that is based on Markov Chain (MC) and Hidden Markov Model (HMM), and compare the accuracy of these solutions using communication sequences annotated with behavior codes from the real-life motivational interviews. Our experiments indicate that the deep learning-based approach is significantly more accurate than the approach based on probabilistic models in predicting the success of motivational interviews (0.8677 versus 0.7038 and 0.6067 F1-score by RNN, MC and HMM, respectively, when using undersampling to correct for class imbalance, and 0.8381 versus 0.7775 and 0.7520 F1-score by RNN, MC and HMM, respectively, when using over-sampling). These results indicate that the proposed method can be used for real-time monitoring of progression of clinical interviews and more efficient identification of effective provider communication strategies, which in turn can significantly decrease the effort required to develop behavioral interventions and increase their effectiveness.