Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network

Abstract

The increasing accumulation of healthcare data provides researchers with ample opportunities to build machine learning approaches for clinical decision support and to improve the quality of health care. Several studies have developed conventional machine learning approaches that rely heavily on manual feature engineering and result in task-specific models for health care. In contrast, healthcare researchers have begun to use deep learning, which has emerged as a revolutionary machine learning technique that obviates manual feature engineering but still achieves impressive results in research fields such as image classification. However, few of them have addressed the lack of the interpretability of deep learning models although interpretability is essential for the successful adoption of machine learning approaches by healthcare communities. In addition, the unique characteristics of healthcare data such as high dimensionality and temporal dependencies pose challenges for building models on healthcare data. To address these challenges, we develop a gated recurrent unit-based recurrent neural network with hierarchical attention for mortality prediction, and then, using the diagnostic codes from the Medical Information Mart for Intensive Care, we evaluate the model. We find that the prediction accuracy of the model outperforms baseline models and demonstrate the interpretability of the model in visualizations.

ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling

“…Electronic Health Records (EHRs) are a large-scale and systematic collection of temporal health information of patients. The broad adoption of EHRs in medical systems has promoted the development of various computational methods for understanding the medical history of patients and predicting risks [Marlin et al, 2012;Choi et al, 2016a;Zhou et al, 2013;Choi et al, 2016b]. In this work, we focus on a task named Disease Progression Modeling (DPM), which monitors the disease developing process and predicts future risks based on patients' historical information.…”

“…DPM is crucial for making clinical decisions and providing prompt medications. A large amount of recent works have been developed for this task [Choi et al, 2016a;Esteban et al, 2016;Lipton et al, 2015;Zhou et al, 2013]. Among them, Recurrent Neural Network (RNN) is one of the most extensively researched deep neural networks to handle the sequential data.…”

“…Among them, Recurrent Neural Network (RNN) is one of the most extensively researched deep neural networks to handle the sequential data. As an extension of RNN, the Long-Short Term Memory (LSTM) is specifically designed to capture long-term patterns that commonly exist over a long period of patients' records [Sundermeyer et al, 2012]. LSTM-based approaches have found a huge success in a variety of tasks involving sequential data, such as video processing, climate changes detecting, and EHRs representation learning [Jia et al, 2019;Lipton et al, 2015;Esteban et al, 2016].…”

Explaining an increase in predicted risk for clinical alerts

“…To explain a patient's risk, statistics of these discretized features can be used such as the odds ratio or the Rothman index [12]. Other lines of work have studied latent Dirichlet allocation [44], convolutional neural networks with feature ablation [45], RNNs with an attention mechanism [46,47], and co-distillation [48,49].…”

New Reality in Clinical Informatics and Explanation-oriented Methods of Data Analysis

“…The illustrative discussion about classification tasks can be found in position paper by Alex A. Freitas [15] and a good survey on interpreting modern machine learning methods can be found in [16]. Some examples of clinical applications of different methods, form simple ANOVA [17] up to recurrent neural network [18], can also be mentioned.…”