Evidence map›Paper›PMID 30537977›Full record

ArticleBMC medical informatics and decision making2018

Improving palliative care with deep learning.

Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H Shah

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
118citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

118 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  13. Palliative care and social practice · 2026
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58 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Anand AvatiDepartment of Computer Science, Stanford University, Stanford, CA, USA. avati@cs.stanford.edu.
Kenneth JungCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Stephanie HarmanDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Lance DowningCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Andrew NgDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Nigam H ShahCenter for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
NCATS NIH HHS UL1 TR001085
6 · The paper itself

Abstract

backgroundAccess 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.

methodsIn 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.

resultsThe 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.

conclusionThe 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.

Indexed as

Clinical Decision-MakingDeep LearningPalliative CarePatient SelectionElectronic Health RecordsHumansPrognosisDeep learningElectronic health recordsInterpretationPalliative care

Identifiers

PMID30537977
PMCPMC6290509

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.