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.

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。