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A machine learning approach to predicting high-risk irritability trajectories across the transition to adolescence

رویکرد یادگیری ماشین برای پیش‌بینی مسیرهای پرخطر تحریک‌پذیری در گذار به نوجوانی

Leslie S. Jordan, Annika J. Quam, Alyssa J. Parker, Jillian Lee Wiggins, Lea Rose Dougherty

Development and Psychopathology 2026 Vol. 2026, pp. 1-11
https://doi.org/10.1017/s0954579426101977

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چکیده

Abstract Youth irritability, characterized as proneness to anger/frustration, is associated with negative mental health outcomes. This study identified irritability trajectories across the transition to early adolescence and applied machine learning to identify preadolescent predictors of irritability trajectories. Data are from the Adolescent Brain Cognitive Development (ABCD) study, a large longitudinal study that tracks youth development from preadolescence ( n = 11,862, ages 8.92–11.08 years, M = 9.91, SD = 0.62). The baseline sample was 47.83% female ( n = 5,680), 63.35% White, 15.74% Black, 12.07% Biracial or Multiracial, 2.32% Asian, and 20.30% Hispanic. Using parent-report youth irritability scores across four years, latent class growth analysis (LCGA) identified four distinct irritability trajectories: low stable ( n = 8,692, 73.28%), high decreasing ( n = 1,083, 9.13%), low increasing ( n = 1,449, 12.22%), and high chronic ( n = 638, 5.38%). Machine learning approaches generated predictive models for irritability trajectories, including baseline youth psychopathology, physical health, neurocognitive abilities, psychosocial environment, demographics, parent psychopathology, and brain structure metrics. The models demonstrated strong performance in detecting probability of being in the high chronic and low stable trajectories but poorer performance in classifying the irritability trajectories characterized by change. The most prominent predictors included youth externalizing and internalizing problems, parent psychopathology, and sleep problems. Findings provide insight into preadolescent predictors of irritability across a critical developmental period.

چکیده به فارسی

چکیده تحریک‌پذیری در نوجوانان، که به عنوان تمایل به خشم یا ناامیدی تعریف می‌شود، با پیامدهای منفی سلامت روان مرتبط است. این مطالعه مسیرهای تحریک‌پذیری را در گذار به اوایل نوجوانی شناسایی کرده و از یادگیری ماشین برای شناسایی پیش‌بینی‌کننده‌های پیش‌نوجوانی مسیرهای تحریک‌پذیری استفاده کرده است. داده‌ها از مطالعه توسعه شناختی مغز نوجوانان (ABCD)، یک مطالعه طولی بزرگ که رشد نوجوانان را از پیش‌نوجوانی دنبال می‌کند (n = 11,862، سنین 8.92 تا 11.08 سال، M = 9.91، SD = 0.62) استخراج شده‌اند. نمونه پایه شامل 47.83٪ دختر (n = 5,680)، 63.35٪ سفیدپوست، 15.74٪ سیاه‌پوست، 12.07٪ دو نژادی یا چند نژادی، 2.32٪ آسیایی و 20.30٪ اسپانیایی‌تبار بود. با استفاده از نمرات تحریک‌پذیری نوجوانان بر اساس گزارش والدین در طول چهار سال، تحلیل رشد کلاس پنهان (LCGA) چهار مسیر متمایز تحریک‌پذیری را شناسایی کرد: پایین پایدار (n = 8,692، 73.28٪)، بالا کاهشی (n = 1,083، 9.13٪)، پایین افزایشی (n = 1,449، 12.22٪) و بالا مزمن (n = 638، 5.38٪). رویکردهای یادگیری ماشین مدل‌های پیش‌بینی برای مسیرهای تحریک‌پذیری ایجاد کردند که شامل آسیب‌شناسی روانی نوجوانان در پایه، سلامت جسمانی، توانایی‌های عصب‌شناختی، محیط روانی-اجتماعی، جمعیت‌شناسی، آسیب‌شناسی روانی والدین و شاخص‌های ساختار مغز بود. مدل‌ها عملکرد قوی در شناسایی احتمال قرار گرفتن در مسیرهای بالا مزمن و پایین پایدار نشان دادند اما در طبقه‌بندی مسیرهای تحریک‌پذیری با تغییر، عملکرد ضعیف‌تری داشتند. برجسته‌ترین پیش‌بینی‌کننده‌ها شامل مشکلات برونی‌سازی و درونی‌سازی نوجوانان، آسیب‌شناسی روانی والدین و مشکلات خواب بودند. یافته‌ها بینش‌هایی درباره پیش‌بینی‌کننده‌های پیش‌نوجوانی تحریک‌پذیری در یک دوره بحرانی رشد ارائه می‌دهد.

ترجمه‌ی خودکار برای آشنایی سریع؛ برای نقل و ارجاع، متن اصلی ملاک است.

موضوع‌ها و کلیدواژه‌ها

سلامت روانشناختنوجوانیSleep disorderanger pronenesschronic irritabilityexternalizing problemsinternalizing problemslate childhoodparental psychopathologytransition to adolescence

ارجاع

Jordan, L. S., Quam, A. J., Parker, A. J., Wiggins, J. L., & Dougherty, L. R. (2026). A machine learning approach to predicting high-risk irritability trajectories across the transition to adolescence. Development and Psychopathology, 2026, 1–11. https://doi.org/10.1017/s0954579426101977

(Jordan et al., 2026)

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گفت‌وگو

قوانین پرسش و گفتگو
  1. با احترام و بدون توهین، تحقیر، برچسب‌زنی یا حمله‌ی شخصی با دیگران گفتگو کنید.
  2. از نوشتن هرگونه اطلاعات هویتی یا قابل‌شناسایی مراجعان، بیماران یا اشخاص ثالث، از جمله نام، شماره‌ی تماس، تصویر، نشانی، محل کار یا جزئیات منحصربه‌فرد پرونده خودداری کنید.
  3. اگر درباره‌ی یک تجربه‌ی بالینی سؤال می‌کنید، اطلاعات را تا حد ممکن بی‌نام و کلی بیان کنید تا امکان شناسایی فرد وجود نداشته باشد.
  4. این بخش برای یادگیری و گفت‌وگوی علمی است و جایگزین ارزیابی، تشخیص، مشاوره یا روان‌درمانی شخصی نیست.
  5. در شرایط بحران یا خطر آسیب به خود یا دیگران، به این بخش اتکا نکنید و برای دریافت کمک فوری با اورژانس اجتماعی ۱۲۳ یا صدای مشاور بهزیستی ۱۴۸۰ تماس بگیرید.
  6. انتشار تبلیغات، معرفی خدمات شخصی، شماره‌ی تلفن، شناسه‌ی شبکه‌های اجتماعی، پیوند تبلیغاتی یا درخواست جذب مراجع در این بخش مجاز نیست.
  7. در بیان ادعاهای علمی، به‌ویژه درباره‌ی اثربخشی درمان‌ها، تشخیص یا یافته‌های پژوهشی، تا حد امکان نام منبع معتبر، مقاله یا راهنمای علمی را ذکر کنید.
  8. از ارائه‌ی تشخیص قطعی، تجویز درمان یا دارو و توصیه‌های بالینی شخصی به سایر کاربران خودداری کنید.
  9. پاسخ‌هایی که کارشناسان فکر بنیان می‌دهند با برچسب «پاسخ فکر بنیان» یا «کارشناس فکر بنیان» نمایش داده می‌شوند و سایر نوشته‌ها بیانگر نظر کاربران هستند.
  10. گزارش نوشته‌ی نامناسب: اگر نوشته‌ای حاوی نقض محرمانگی، محتوای آسیب‌زا، توهین، تبلیغ یا تخلف از این قوانین است، گزینه‌ی «گزارش» را انتخاب کنید تا تیم سایت بررسی کند.

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