Peng Jin, Chen Jiayuan, Yin Changchang, Zhang Ping, Yang Jingzhen
Information Technology Research and Innovation, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA.
Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:422-431. eCollection 2025.
Youth who experience concussions may be at greater risk for subsequent mental health challenges, making early detection crucial for timely intervention. This study utilized Bidirectional Long Short-Term Memory (BiLSTM) networks to predict mental health outcomes following concussion in youth and compared its performance to traditional models. We also examined whether incorporating social determinants of health (SDoH) improved predictive power, given the disproportionate impact of concussions and mental health issues on disadvantaged populations. We evaluated the models using accuracy, area under the curve (4UC) of the receiver operating characteristic (ROC), and other performance metrics. Our BiLSTM model with SDoH data achieved the highest accuracy (0.883) and 4UC-ROC score (0.892). Unlike traditional models, our approach provided real-time predictions at each visit within 12 months of the index concussion, aiding clinicians in making timely, visit-specific referrals for further treatment and interventions.
经历脑震荡的青少年可能面临更大的后续心理健康挑战风险，因此早期检测对于及时干预至关重要。本研究利用双向长短期记忆（BiLSTM）网络预测青少年脑震荡后的心理健康结果，并将其性能与传统模型进行比较。鉴于脑震荡和心理健康问题对弱势群体的影响不成比例，我们还研究了纳入健康的社会决定因素（SDoH）是否能提高预测能力。我们使用准确率、受试者工作特征曲线（ROC）下的面积（AUC）和其他性能指标对模型进行评估。我们包含SDoH数据的BiLSTM模型实现了最高准确率（0.883）和AUC-ROC分数（0.892）。与传统模型不同，我们的方法在首次脑震荡后的12个月内每次就诊时都能提供实时预测，帮助临床医生针对每次就诊及时进行特定转诊，以便进一步治疗和干预。
