论文《Learning to Diagnose with LSTM Recurrent Neural Networks》，临床医学数据，特别是在重症监护病房（ICU），由多变量时间序列的观察组成。对于每个患者，传感器数据和实验室测试结果，均记录在患者的电子健康记录（EHR）中。虽然可能包含大量的见解，但由于长度的不一致，抽样的不规则和数据的缺失，数据难以有效挖掘。RNN，特别是使用长短期存储器（LSTM）隐藏单元的类似技术，可以从序列数据学习，是一种非常强大且越来越流行的模型。他们有效地建模不同长度的序列，并捕获长距离依赖性。我们第一个提出研究经验评估LSTMs识别多变量时间序列的临床测量模式的能力。具体来说，我们考虑诊断的多标签分类，训练模型分类128种诊断。首先，我们验证了一个简单的LSTM网络的临床数据建模的有效性。然后我们演示一个直接和有效的训练策略。我们仅对原始时间序列进行训练，我们的模型优于几个强大的基线，包括对手工工程特征进行训练的多层感知器。
摘要： Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features.
