论文标题
Medi-Care AI：通过强大的经常性神经网络预测计费代码的药物
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
论文作者
论文摘要
在本文中，我们提出了一个有效的深度预测框架，基于强大的复发神经网络（RNN），以预测患者正在服用的药物的可能治疗类型，鉴于其记录中的一系列诊断计费代码。由于错误和遗漏，准确地捕获给定患者当前服用的药物清单极具挑战性。我们提出了一个一般的鲁棒框架，该框架通过加班费机制在输入计费代码和噪声注射中分别对复发性隐藏状态进行显式污染。通过这样做，计费代码被重新制定为其时间模式，每个医疗变量上的衰减率都被衰减率，并且RNN的隐藏状态通过随机噪声正规化，这些噪声可作为辍学，以提高RNNS在缺失值和多个错误方面对数据变异性的稳健性。对所提出的方法进行了对实际医疗保健数据的广泛评估，以证明其在暗示受污染值的药物订单方面的有效性。
In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularised by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.
