Improving the quality of end-of-life care for hospitalized patients is a
priority for healthcare organizations. Studies have shown that physicians tend
to over-estimate prognoses, which in combination with treatment inertia results
in a mismatch between patients wishes and actual care at the end of life. We
describe a method to address this problem using Deep Learning and Electronic
Health Record (EHR) data, which is currently being piloted, with Institutional
Review Board approval, at an academic medical center. The EHR data of admitted
patients are automatically evaluated by an algorithm, which brings patients who
are likely to benefit from palliative care services to the attention of the
Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR
data from previous years, to predict all-cause 3-12 month mortality of patients
as a proxy for patients that could benefit from palliative care. Our
predictions enable the Palliative Care team to take a proactive approach in
reaching out to such patients, rather than relying on referrals from treating
physicians, or conduct time consuming chart reviews of all patients. We also
present a novel interpretation technique which we use to provide explanations
of the model's predictions.Comment: IEEE International Conference on Bioinformatics and Biomedicine 201
