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中国人の青少年における非自殺的自己傷害の長期的機械学習予測:前向きな多センターコホート研究

中国人の青少年における非自殺的自己傷害の長期的機械学習予測:前向きな多センターコホート研究

Xinyu Guo ¹, Shuyi Liu ², Lihua Jiang ³, Zhihan Xiong ², Linna Wang ⁴, Li Lu ⁴, Xiang Li ⁵, Li Zhao ², Daniel T L Shek ⁵

Xinyu Guo ¹, Shuyi Liu ², Lihua Jiang ³

¹Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region of China.
²Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
³Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Teaching and Research Section of General Practice, The General Practice Medical Center, West China Hospital of Sichuan University, Chengdu, China.
⁴College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
⁵Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region of China.

⁰Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region of China.

Journal of Affective Disorders +

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2025年8月28日

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PubMedで要約を見る

まとめ

この要約は機械生成です。

機械学習モデルは縦断データを使って青少年の非自殺自傷 (NSSI) を正確に予測します精神的・感情的な能力が重要な保護要因であり標的型予防戦略を導きます

科学分野

青少年の精神的健康
心理学における機械学習
公衆衛生研究

背景

非自殺自傷 (NSSI) は若者の公衆衛生上の大きな課題です.
NSSIの伝統的な予測方法は十分な精度がない.
縦断的なデータ分析は,NSSIの発展を理解するために不可欠です.

研究の目的

青少年におけるNSSIを予測するための機械学習モデルを開発し評価する.
特徴の重要性分析を用いて,NSSIに影響を与える主要なリスクと保護要因を特定する.
漸進的な枠組みを通じてNSSI予測の精度を高めること.

主な方法

2年半にわたる中国人の小中学生の3,483人の経度データを使用した.
7つの機械学習アルゴリズムと5倍のクロス検証を用いた漸進的な予測フレームワークを使用した.
クラス不均衡のSMOTE-NCと,モデル解釈のSHAPを適用した.

主要な成果

ランダムフォレストは優れた予測性能を示した (AUROC ≈ 0.85).
自殺に関連した行動,鬱,犯罪,不安を主な危険因子として特定しました.
霊性,感情的能力,人生への満足感,そして共感が重要な保護要因として強調されました.

結論

縦断的なNSSI予測のための強固で解釈可能な機械学習の枠組みが確立されました.
NSSIのリスクを軽減するために,特に精神性や感情的な能力といった子どもの発達要因は極めて重要です.
発見は,若者のNSSIの早期介入と予防のための証拠に基づいた戦略を支持しています.

キーワード:

青少年長距離コホート研究機械学習自殺目的のない自己傷害

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