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Article overview
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Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset | | Sunil Mallya
; Marc Overhage
; Navneet Srivastava
; Tatsuya Arai
; Cole Erdman
; | | Date: |
7 Feb 2019 | | Abstract: | In this paper we present a Recurrent neural networks (RNN) based architecture
that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart
Failure (CHF) 15 months in advance using a 12-month observation window on a
large cohort of 216,394 patients. We believe this to be the largest study in
CHF onset prediction with respect to the number of CHF case patients in the
cohort and the test set (3,332 CHF patients) on which the AUC metrics are
reported. We explore the extent to which LSTM (Long Short Term Memory) based
model, a variant of RNNs, can accurately predict the onset of CHF when compared
to known linear baselines like Logistic Regression, Random Forests and deep
learning based models such as Multi-Layer Perceptron and Convolutional Neural
Networks. We utilize demographics, medical diagnosis and procedure data from
21,405 CHF and 194,989 control patients to as our features. We describe our
feature embedding strategy for medical diagnosis codes that accommodates the
sparse, irregular, longitudinal, and high-dimensional characteristics of EHR
data. We empirically show that LSTMs can capture the longitudinal aspects of
EHR data better than the proposed baselines. As an attempt to interpret the
model, we present a temporal data analysis-based technique on false positives
to attribute feature importance. A model capable of predicting the onset of
congestive heart failure months in the future with this level of accuracy and
precision can support efforts of practitioners to implement risk factor
reduction strategies and researchers to begin to systematically evaluate
interventions to potentially delay or avert development of the disease with
high mortality, morbidity and significant costs. | | Source: | arXiv, 1902.2443 | | Services: | Forum | Review | PDF | Favorites | |
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