A machine learning-based discriminative framework for mild cognitive impairment risk in middle-aged and elderly Chinese patients with depression: evidence from CHARLS and CLHLS.

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Title: A machine learning-based discriminative framework for mild cognitive impairment risk in middle-aged and elderly Chinese patients with depression: evidence from CHARLS and CLHLS.
Authors: Yao, Jin1,2,3 (AUTHOR), Chen, Yan2,3 (AUTHOR), Huang, Zhi-can2,3 (AUTHOR), Chen, Zi-wen2,3 (AUTHOR), Yin, Zi-han1,2,3 (AUTHOR) yinzihan@stu.cdutcm.edu.cn, Liang, Fan-rong1,2,3 (AUTHOR) acuresearch@126.com
Source: Journal of Affective Disorders. Feb2026:Part B, Vol. 394, pN.PAG-N.PAG. 1p.
Database: Academic Search Ultimate
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  Data: A machine learning-based discriminative framework for mild cognitive impairment risk in middle-aged and elderly Chinese patients with depression: evidence from CHARLS and CLHLS.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Affective+Disorders%22">Journal of Affective Disorders</searchLink>. Feb2026:Part B, Vol. 394, pN.PAG-N.PAG. 1p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=189647961
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.jad.2025.120701
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Titles:
      – TitleFull: A machine learning-based discriminative framework for mild cognitive impairment risk in middle-aged and elderly Chinese patients with depression: evidence from CHARLS and CLHLS.
        Type: main
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            NameFull: Yao, Jin
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            NameFull: Chen, Yan
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            NameFull: Huang, Zhi-can
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            NameFull: Chen, Zi-wen
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            NameFull: Yin, Zi-han
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            NameFull: Liang, Fan-rong
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          Dates:
            – D: 15
              M: 02
              Text: Feb2026:Part B
              Type: published
              Y: 2026
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              Value: 01650327
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              Value: 394
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            – TitleFull: Journal of Affective Disorders
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