Evidence map›Paper›PMID 38040784›Full record

ArticleScientific reports2023

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning.

Andrew Tritt, John K Yue, Adam R Ferguson, Abel Torres Espin, Lindsay D Nelson, Esther L Yuh, Amy J Markowitz, Geoffrey T Manley, Kristofer E Bouchard, TRACK-TBI Investigators

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 15% of its field
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

5 citing papers in PubMed, 8 citations in OpenAlex.

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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

10 authors at 8 institutions in 1 country.

Andrew TrittApplied Math and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
John K YueBrain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
Adam R FergusonBrain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
Abel Torres EspinBrain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
Lindsay D NelsonDepartments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA.
Esther L YuhBrain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
Amy J MarkowitzBrain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
Geoffrey T ManleyBrain and Spinal Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA.
Kristofer E BouchardWeill Neurohub, University of California Berkeley, Berkeley, CA, USA. kebouchard@lbl.gov.
TRACK-TBI Investigators
San Francisco General Hospital · USUniversity of California, San Francisco · USBaylor College of Medicine · USHarvard University · USLawrence Berkeley National Laboratory · USMedical College of Wisconsin · USUniversity of Washington · USVirginia Commonwealth University · US

Funding

Transforming Research and Clinical Knowledge in Traumatic Brain InjuryU01NS086090 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI DIAZ-ARRASTIA, RAMON, GIACINO, JOSEPH THOMAS · 2013 to 2017
$19.1M
Transforming Traumatic Brain Injury Research and Clinical CareRC2NS069409 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MANLEY, GEOFFREY T · 2009 to 2011
$4.3M
NINDS NIH HHS RC2 NS069409NINDS NIH HHS RC2NS069409, U01NS086090, U01NS1365885NINDS NIH HHS U01 NS086090
6 · The paper itself

Abstract

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

Indexed as

Brain Injuries, TraumaticDistillationHumansMachine LearningPhenotypePrognosis

Identifiers

PMID38040784
PMCPMC10692236
OpenAlexW4389236275

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.