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Clinical medical data, especially in the intensive care unit (ICU), consistof multivariate time series of observations. For each patient visit (orepisode), sensor data and lab test results are recorded in the patient'sElectronic Health Record (EHR). While potentially containing a wealth ofinsights, 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, arepowerful and increasingly popular models for learning from sequence data. Theyeffectively model varying length sequences and capture long range dependencies.We present the first study to empirically evaluate the ability of LSTMs torecognize patterns in multivariate time series of clinical measurements.Specifically, we consider multilabel classification of diagnoses, training amodel to classify 128 diagnoses given 13 frequently but irregularly sampledclinical measurements. First, we establish the effectiveness of a simple LSTMnetwork for modeling clinical data. Then we demonstrate a straightforward andeffective training strategy in which we replicate targets at each sequencestep. Trained only on raw time series, our models outperform several strongbaselines, including a multilayer perceptron trained on hand-engineeredfeatures.

Learning to Diagnose with LSTM Recurrent Neural Networks