Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Diagnose with LSTM Recurrent Neural Networks   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.

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The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph