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. 2018 Dec 12;18(Suppl 4):122.
doi: 10.1186/s12911-018-0677-8.

Improving palliative care with deep learning

Anand Avati  1 , Kenneth Jung  2 , Stephanie Harman  3 , Lance Downing  2 , Andrew Ng  4 , Nigam H Shah  2
Affiliations

Improving palliative care with deep learning

Anand Avati et al. BMC Med Inform Decis Mak. .

Abstract

Background: Access to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life.

Methods: In this work, we address this problem, with Institutional Review Board approval, using machine learning and Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of patients from previous years, to predict mortality of patients within the next 3-12 month period. This prediction is used as a proxy decision for identifying patients who could benefit from palliative care.

Results: The EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team is automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique for decision interpretation, using which we provide explanations for the model's predictions.

Conclusion: The automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then relying on referrals from the treating physicians.

Keywords: Deep learning; Electronic health records; Interpretation; Palliative care.

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Conflict of interest statement

Ethics approval and consent to participate

This work has obtained ethics approval by the Institutional Review Board of Stanford University under protocol 42078.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Right-censoring lengths shown as a survival plot
Fig. 2
Fig. 2
Age of patients at prediction time
Fig. 3
Fig. 3
Reliability curve (calibration plot) of the model output probabilities on the test set data
Fig. 4
Fig. 4
Interpolated Precision-Recall curve. The horizontal dotted line represents precision level of 0.9. The vertical dotted lines indicate the recall at which the curves achieve 0.9 precision
Fig. 5
Fig. 5
Receiver Operating Characteristic (ROC) of the model performance on the test set
See this image and copyright information in PMC

References

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