機械学習ベースのコンピュータ適応型テスト（ML-CAT）を用いたマッカーサー・ベイツ発達検査（MacArthur-Bates Communicative Developmental Inventory）の短縮化

Diana Saker, Haya Salameh, Hila Gendler-Shalev

IEEE journal of biomedical and health informatics

|February 11, 2026

まとめ

機械学習とコンピュータ適応型テスト（ML-CAT）の組み合わせにより、マッカーサー・ベイツ発達検査（MB-CDI）を早期語彙発達評価のために大幅に短縮し、効率と信頼性を向上させる。

科学分野:

発達心理学
計算言語学
機械学習

背景:

乳幼児の発達障害の早期特定は、効果的な介入とコスト削減のために重要です。
マッカーサー・ベイツ発達検査（MB-CDI）は、早期語彙発達を評価するための標準的なツールですが、長くて時間がかかります。
既存の評価方法には、効率と適応性に関する限界があります。

主な方法:

機械学習アプローチとコンピュータ適応型テスト（ML-CAT）を組み合わせて利用しました。
個々の被験者の応答に基づいて提示される単語の順序を適応させ、評価を短縮しました。
ML-CATを5つの異なる言語で検証しました。

結論:

ML-CATは、乳幼児の語彙発達を評価するためのより効率的で信頼性の高い方法を提供します。
評価の短縮により、被験者と介護者の負担が軽減され、信頼性が向上する可能性があります。
ML-CATは、発達障害のより広範で頻繁な地域ベースのスクリーニングを促進します。

キーワード:

コンピュータ適応型テスト機械学習語彙発達乳幼児発達障害スクリーニング

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