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2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
ISBN: 978-1-5090-3051-4
pp: 311-316
Anand Avati , Dept of Computer Science, Stanford University
Kenneth Jung , Center for Biomedical Informatics Research, Stanford University
Stephanie Harman , Dept of Medicine, Stanford University School of Medicine
Lance Downing , Center for Biomedical Informatics Research, Stanford University
Andrew Ng , Dept of Computer Science, Stanford University
Nigam H. Shah , Center for Biomedical Informatics Research, Stanford University
ABSTRACT
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.
INDEX TERMS
Hospitals, Diseases, Machine learning, Data models, History, Training
CITATION

A. Avati, K. Jung, S. Harman, L. Downing, A. Ng and N. H. Shah, "Improving palliative care with deep learning," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 311-316.
doi:10.1109/BIBM.2017.8217669
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