GRAM

作者：Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun · 年份：2017 · DOI：10.1145/3097983.3098126 · 被引用次数：670 · 研究领域：Machine Learning in Healthcare、Artificial Intelligence in Healthcare、Topic Modeling

accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.