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LIG-Doctor: Efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks
Information Sciences Pub Date : 2020-09-20 , DOI: 10.1016/j.ins.2020.09.024
Jose F. Rodrigues-Jr , Marco A. Gutierrez , Gabriel Spadon , Bruno Brandoli , Sihem Amer-Yahia

The interest for patient trajectory prediction, a sort of computer-aided medicine, has steadily increased with the pace of artificial intelligence innovation. Notwithstanding, the design of effective systems able to predict clinical outcomes based on the history of a patient is far from trivial. Works so far are based on neural architectures with low performance, especially when using low-cardinality datasets; alternatively, complex inference approaches are hard to reproduce and/or extrapolate as they are designed for very specific circumstances. We introduce LIG-Doctor, an artificial neural network architecture based on two Minimal Gated Recurrent Unit networks functioning in a bidirectional parallel manner, benefiting from temporal events both forward and backward. In comparison to state-of-the-art works, consistent improvements were achieved in prognosis prediction, as assessed with metrics Recall@k, Precision@k, F1-score, and AUC-ROC. Besides the detailed delineation of our architecture, a sequence of experiments is reported with insights that progressively guided design decisions to inspire future works on similar problems. Our results shall contribute to the improvement of computer-aided medicine and, more generally, to processes related to the design of neural network architectures.



中文翻译:

LIG-Doctor:使用双向最小门控递归网络进行有效的患者轨迹预测

随着人工智能创新的步伐,对患者轨迹预测(一种计算机辅助医学)的兴趣稳步增长。尽管如此,能够根据患者的病史预测临床结果的有效系统的设计绝非易事。迄今为止的工作都是基于性能低下的神经体系结构,尤其是在使用低基数数据集时。另外,复杂的推理方法很难重现和/或推断,因为它们是针对非常特定的情况而设计的。我们介绍了LIG-Doctor,这是一种基于两个最小门控递归单元网络的人工神经网络体系结构,双向双向并行运行,得益于向前和向后的时间事件。与最先进的作品相比,如指标Recall @ k,Precision @ k,F1-score和AUC-ROC所评估的,预后预测获得了一致的改善。除了详细描述我们的体系结构外,还报告了一系列实验,这些实验具有深刻的见解,这些见解逐步指导了设计决策,以启发人们就类似问题开展未来的工作。我们的研究结果将有助于改善计算机辅助医学,更广泛地说,将有助于与神经网络体系结构设计相关的过程。

更新日期:2020-09-20
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