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ティーンエイジントの非自殺的自己傷害の解読:解釈可能な機械学習の洞察による理解

Haojie Fu1,2, Mengmeng Zhang3, Shuran Yang4

  • 1Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Siping Road, Shanghai, 200092, Shanghai, China.

BMC public health
|September 1, 2025
PubMed
まとめ

機械学習モデルは,若者の非自殺自傷 (NSSI) リスク要因を効果的に特定します. 主要な要素は不安,うつ,自尊心,人間関係の問題であり,統合理論モデルを洗練しています.

キーワード:
探索的要素分析統合された理論モデル機械学習自殺 目的 の ない 自己 傷害SHAP ビジュアル化

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科学分野:

  • 青春期の心理学
  • 計算精神科
  • 行動科学

背景:

  • 非自殺自傷 (NSSI) は 流行しているが 挑戦的な青少年リスク行動です
  • 早期発見と介入はNSSIの影響を軽減するために不可欠です.
  • 効果的な戦略を策定するには 根本的なリスクと保護要因を理解することが不可欠です

研究 の 目的:

  • 青少年NSSIのための解釈可能な機械学習分類モデルを開発する.
  • NSSIに関連する重要なリスクと保護要因を特定する.
  • 統合理論モデルの枠組みの中でこれらの要因を評価する.

主な方法:

  • 中国東部で2989人の青少年からアンケートで収集したデータです.
  • 6つの機械学習アルゴリズムが適用された:KNN,SVM,ロジスティック回帰,LGBM,CatBoost,XGBoost.
  • 重要な要因を特定するために使用されるSHAP可視化と探査因数分析.

主要な成果:

  • CatBoostアルゴリズムは優れた性能を示した (AUPRC=0. 736,AUC=0. 863).
  • SHAPの分析では,NSSIに影響を与える23の重要な項目を強調しました.
  • 7つの要因 が 特定 さ れ まし た.状況 に 関する 不安,うつ の 症状,日々の 行動 に 関する 積極 的 な 態度,自己 評価 の 否定 的 な 態度,いじめ や 反応 的 な 攻撃,人間 関係 の 問題 と 自己 受け入れ など です.

結論:

  • 機械学習は複雑なNSSIデータを分析するための強力なアプローチを提供します.
  • 特定された要因は,NSSIの統合理論モデルを精進するための洞察を提供します.
  • この研究は,青少年におけるNSSIの理解を深め,標的型介入を支援します.