Predicting the Outcome of Patient-Provider Communication Sequences using Recurrent Neural Networks and Probabilistic Models

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.