Comparison and interpretability of machine learning models to predict severity of chest injury is a research paper published in JAMIA Open (2021). On theSindex it has a DataRank of 0.406. It has been cited 14 times.
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Base Score Contribution
0.406
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.
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Grant: K23 AA024503
NIH
Grant: R01 DA051464
National Institute of General Medical Sciences
Grant: R01 GM123193
EarlySense
Grant: ARCD. P0535US.P2
National Library of Medicine of the National Institutes of Health
Grant: R01LM012973
National Library of Medicine of the National Institutes of Health
Grant: R01LM010090
NIH National Institute on Alcohol Abuse and Alcoholism
Grant: T32 AA1352719
NHLBI NIH HHS
Grant: K23 HL146890
National Institutes of Health
Grant: 5K23AA024503-02
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National Institutes of Health
Grant: 5R01LM010090-02
Temporal relation discovery for clinical text
National Institutes of Health
Grant: 1R01LM012973-01
Learning Universal Patient Representations from Clinical Text with Hierarchical Recurrent Neural Networks
National Institutes of Health
Grant: 1R01GM123193-01
Sepsis Early Prediction and Subphenotype Illumination Study (SEPSIS)
National Institutes of Health
Grant: 3R01DA051464-02S1
Building a Substance Use Data Commons for Public Health Informatics
National Institutes of Health
Fields of Study
Keywords
Sustainable Development Goals