Fairness in the prediction of acute postoperative pain using machine learning models is a research paper published in Frontiers in Digital Health (2023). On theSindex it has a DataRank of 0. It has been cited 24 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?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Natural Science Foundation of China
Grant: 1750192, R01EB029699, R21EB027344
National Institute of Biomedical Imaging and Bioengineering
Grant: R01NS120924
National Institute of Neurological Disorders and Stroke
Grant: K07 AG073468, R01 AG121647
NIBIB NIH HHS
Grant: R21 EB027344
NIA NIH HHS
Grant: K07 AG066813
NIBIB NIH HHS
Grant: R01 EB029699
NIA NIH HHS
Grant: K07 AG073468
NIA NIH HHS
Grant: R01 AG055337
National Institutes of Health
Grant: 5K07AG073468-03
Perioperative Cognitive Anesthesia Network Extension for Socially Vulnerable Older Adults
National Science Foundation
Grant: 1750192
CAREER: Fundamental Intelligent Building Blocks of the Intensive Care Unit (ICU) of the Future
National Institutes of Health
Grant: 5R01EB029699-02
Intelligent Intensive Care Unit (I2CU): Pervasive Sensing and Artificial Intelligence for Augmented Clinical Decision-making
National Institutes of Health
Grant: 5R21EB027344-02
Autonomous Pain Recognition in Non-Verbal and Critically Ill Patients
National Institutes of Health
Grant: 5R01NS120924-03
ADAPT: Autonomous Delirium Monitoring and Adaptive Prevention
Fields of Study
Keywords