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：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes
    Saffari, Seyed Ehsan
    Ning, Yilin
    Xie, Feng
    Chakraborty, Bibhas
    Volovici, Victor
    Vaughan, Roger
    Ong, Marcus Eng Hock
    Liu, Nan
    BMC MEDICAL RESEARCH METHODOLOGY, 2022, 22 (01)
  • [2] Deep and interpretable regression models for ordinal outcomes
    Kook, Lucas
    Herzog, Lisa
    Hothorn, Torsten
    Durr, Oliver
    Sick, Beate
    PATTERN RECOGNITION, 2022, 122
  • [3] A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes
    Xie, Feng
    Ning, Yilin
    Liu, Mingxuan
    Li, Siqi
    Saffari, Seyed Ehsan
    Yuan, Han
    Volovici, Victor
    Ting, Daniel Shu Wei
    Goldstein, Benjamin Alan
    Ong, Marcus Eng Hock
    Vaughan, Roger
    Chakraborty, Bibhas
    Liu, Nan
    STAR PROTOCOLS, 2023, 4 (02):
  • [4] Ordinal extreme learning machine
    Deng, Wan-Yu
    Zheng, Qing-Hua
    Lian, Shiguo
    Chen, Lin
    Wang, Xin
    NEUROCOMPUTING, 2010, 74 (1-3) : 447 - 456
  • [5] Regression Models for Ordinal Outcomes
    French, Benjamin
    Shotwell, Matthew S.
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2022, 328 (08): : 772 - 773
  • [6] Evolutionary Ordinal Extreme Learning Machine
    Sanchez-Monedero, Javier
    Antonio Gutierrez, Pedro
    Hervas-Martinez, Cesar
    HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, 2013, 8073 : 500 - 509
  • [7] Classification by ordinal sums of conjunctive and disjunctive functions for explainable AI and interpretable machine learning solutions
    Hudec, Miroslav
    Minarikova, Erika
    Mesiar, Radko
    Saranti, Anna
    Holzinger, Andreas
    KNOWLEDGE-BASED SYSTEMS, 2021, 220
  • [8] Sparse Kernel Machine Regression for Ordinal Outcomes
    Shen, Yuanyuan
    Liao, Katherine P.
    Cai, Tianxi
    BIOMETRICS, 2015, 71 (01) : 63 - 70
  • [9] A comprehensive framework of regression models for ordinal data
    Iannario, Maria
    Piccolo, Domenico
    METRON-INTERNATIONAL JOURNAL OF STATISTICS, 2016, 74 (02): : 233 - 252
  • [10] Evaluating the Effectiveness of Accommodations Given to Students With Learning Impairments: Ordinal and Interpretable Machine-Learning-Based Methodology
    Singer, Gonen
    Golan, Maya
    Shiff, Rachel
    Kleper, Dvir
    IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 2022, 15 (06): : 736 - 746
12345