Taipei Medical University Institutional Repository:Item 987654321/65165
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    Please use this identifier to cite or link to this item: http://libir.tmu.edu.tw/handle/987654321/65165


    Title: Machine Learning-based Brief Version of the Caregiver-Teacher Report Form for Preschoolers
    Authors: 林恭宏
    Gong-Hong Lin, Shih-Chieh Lee, Yen-Ting Yu, Chien-Yu Huang
    Contributors: 國際高齡健康暨長期照護博士學位學程
    Keywords: Keywords: Artificial intelligence;Machine learning;Assessment;Emotional and behavioral problems
    Date: 2023-03
    Issue Date: 2025-03-27 10:36:25 (UTC+8)
    Abstract: Abstract: Background
    The Caregiver-Teacher Report Form of the Child Behavior Checklist for Ages 1?–5 (C-TRF) is a widely used checklist to identify emotional and behavioral problems in preschoolers. However, the 100-item C-TRF restricts its utility.
    Aims
    This study aimed to develop a machine learning-based short-form of the C-TRF (C-TRF-ML).
    Methods and procedures
    Three steps were executed. First, we split the data into three datasets in a ratio of 3:1:1 for training, validation, and cross-validation, respectively. Second, we selected a shortened item set and trained a scoring algorithm using joint learning for classification and regression using the training dataset. Then, we evaluated the similarity of scores between the C-TRF-ML and the C-TRF by r-squared and weighted kappa values using the validation dataset. Third, we cross-validated the C-TRF-ML by calculating the r-squared and weighted kappa values using the cross-validation dataset.
    Outcomes and results
    Data of 363 children were analyzed. Thirty-six items of the C-TRF were retained. The r-squared values of C-TRF-ML scores were 0.86–0.96 in the cross-validation dataset. Weighted kappa values of the syndrome/problem grading were 0.72–0.94 in the cross-validation dataset.
    Conclusions and implications
    The C-TRF-ML had about 60 % fewer items than the C-TRF but yielded comparable scores with the C-TRF.
    Relation: Research in Developmental Disabilities, Volume 134, 2023, 104437
    Description: 【112-1 升等】臺北醫學大學教師升等專門著作
    職別:專任
    送審等級:副教授
    著作送審
    Data Type: article
    Appears in Collections:[Scholarly output for promotion] 112
    [International Ph.D. Program in Gerontology and Long-Term Care] article

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