Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study is a research paper published in Frontiers in Pediatrics (2023). On theSindex it has a DataRank of 0.373. It has been cited 11 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
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Base Score Contribution
0.373
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →NICHD
Grant: 5T32HD040686
NICHD
Grant: 1K23HD099331-01A1
A Learning Health System Approach to Precision Sedation and Analgesia in Critically-Ill Children
NICHD NIH HHS
Grant: K23 HD099331
NICHD NIH HHS
Grant: T32 HD040686
National Institutes of Health
Grant: 5T32HD040686-15
Pediatric Neurointensive Care and Resuscitation Research
NIH
Fields of Study
Keywords