An Interpretable Deep-Learning Framework for Predicting Hospital
  Readmissions From Electronic Health Records
Fabio Azzalini Tommaso Dolci Marco Vagaggini
With the increasing availability of patients' data, modern medicine is
shifting towards prospective healthcare. Electronic health records contain a
variety of information useful for clinical patient description and can be
exploited for the construction of predictive models, given that similar medical
histories will likely lead to similar progressions. One example is unplanned
hospital readmission prediction, an essential task for reducing hospital costs
and improving patient health. Despite predictive models showing very good
performances especially with deep-learning models, they are often criticized
for the poor interpretability of their results, a fundamental characteristic in
the medical field, where incorrect predictions might have serious consequences
for the patient health. In this paper we propose a novel, interpretable
deep-learning framework for predicting unplanned hospital readmissions,
supported by NLP findings on word embeddings and by neural-network models
(ConvLSTM) for better handling temporal data. We validate our system on the two
predictive tasks of hospital readmission within 30 and 180 days, using
real-world data. In addition, we introduce and test a model-dependent technique
to make the representation of results easily interpretable by the medical
staff. Our solution achieves better performances compared to traditional models
based on machine learning, while providing at the same time more interpretable
results.