  Heart failure (HF) is one of the leading causes of hospital admissions in the
US. Readmission within 30 days after a HF hospitalization is both a recognized
indicator for disease progression and a source of considerable financial burden
to the healthcare system. Consequently, the identification of patients at risk
for readmission is a key step in improving disease management and patient
outcome. In this work, we used a large administrative claims dataset to
(1)explore the systematic application of neural network-based models versus
logistic regression for predicting 30 days all-cause readmission after
discharge from a HF admission, and (2)to examine the additive value of
patients' hospitalization timelines on prediction performance. Based on data
from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and
343,328 HF admissions (67% of total admissions), we trained and tested our
predictive readmission models following a stratified 5-fold cross-validation
scheme. Among the deep learning approaches, a recurrent neural network (RNN)
combined with conditional random fields (CRF) model (RNNCRF) achieved the best
performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645).
Other models, such as those based on RNN, convolutional neural networks and CRF
alone had lower performance, with a non-timeline based model (MLP) performing
worst. A competitive model based on logistic regression with LASSO achieved a
performance of 0.643 AUC (95%CI, 0.640-0.646). We conclude that data from
patient timelines improve 30 day readmission prediction for neural
network-based models, that a logistic regression with LASSO has equal
performance to the best neural network model and that the use of administrative
data result in competitive performance compared to published approaches based
on richer clinical datasets.
