Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network
By Ying Sha, May D. Wang. 2017
103 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Amitojdeep Singh
- Slobodan Vucetic
- Tian Bai
- Shanshan Zhang
- Vasudevan Lakshminarayanan
- Brian L. Egleston
- Sourya Sengupta
In Signal Processing
Citations in the corpus
- Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, methodology): “Previous methods use CNN filter [10] or RNN units [28] to generate the context vector.”
- Explainable Deep Learning Models in Medical Image Analysis (2020, background): “cause of the detailed information presented by the images. However, patient records also provide rich information for diagnosis and there were studies discussing their explainability. For example, in [67] a gated recurrent unit (GRU)-based recurrent neural network (RNN) for mortality prediction from”