AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes

被引：0
|
作者：
Seyed Ehsan Saffari
Yilin Ning
Feng Xie
Bibhas Chakraborty
Victor Volovici
Roger Vaughan
Marcus Eng Hock Ong
Nan Liu
机构：
[1] Duke-NUS Medical School,Centre for Quantitative Medicine
[2] Duke-NUS Medical School,Programme in Health Services and Systems Research
[3] Duke University,Department of Biostatistics and Bioinformatics
[4] National University of Singapore,Department of Statistics and Data Science
[5] Erasmus MC University Medical Center,Department of Neurosurgery
[6] Erasmus MC,Department of Public Health
[7] Singapore General Hospital,Department of Emergency Medicine
[8] Singapore Health Services,SingHealth AI Office
[9] Institute of Data Science,undefined
[10] National University of Singapore,undefined
来源：
BMC Medical Research Methodology | / 22卷
关键词：
Interpretable machine learning; Medical decision making; Clinical score; Ordinal outcome; Electronic health records;
D O I：
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