摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx编辑来自伊利诺伊州爱德华斯维尔的新闻，这项研究指出：“不良童年经历(ACEs)包括一系列虐待、忽视和功能失调的家庭行为，这些行为与长期健康问题、心理健康状况和社会困难密切相关。”这项研究的财政支持者包括国家科学基金会。新闻记者从南伊利诺伊大学爱德华斯维尔分校的研究中获得了一句话：“这项研究旨在揭示影响0-17岁儿童ACE发生的重要因素，并提出一个预测模型，用于预测儿童ACE发生的可能性。机器学习模型被用于识别和分析几个预测因素与ACE发生的关系。”使用AUC、F1得分、Recal L和精度等关键性能指标来评估不同事实Rs对ACEs的预测强度。家庭结构，特别是单一Parenting等非传统形式，以及重新定位到新地址的频率被确定为ACEs的关键预测因子。最终的神经网络模型AUC为0.788，精度得分为0.683，该模型的ROC曲线和PR曲线对两个或两个以上ACE患儿的检出率较高,但对单个ACE病例的准确分类存在困难。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Edwardsville, Illinois, by NewsRx editors, the research stated, "Adverse childhood experiences (ACEs) inclu de a range of abusive, neglectful, and dysfunctional household behaviors that ar e strongly associated with long-term health problems, mental health conditions, and societal difficulties." Financial supporters for this research include National Science Foundation. The news journalists obtained a quote from the research from Southern Illinois U niversity Edwardsville: "The study aims to uncover significant factors influenci ng ACEs in children aged 0-17 years and to propose a predictive model that can b e used to forecast the likelihood of ACEs in children. Machine learning models a re applied to identify and analyze the relationships between several predictors and the occurrence of ACEs. Key performance metrics such as AUC, F1 score, recal l, and precision are used to evaluate the predictive strength of different facto rs on ACEs. Family structures, especially non-traditional forms such as single p arenting, and the frequency of relocating to a new address are determined as key predictors of ACEs. The final model, a neural network, achieved an AUC of 0.788 , a precision score of 0.683, and a recall of 0.707, indicating its effectivenes s in accurately identifying ACE cases. The model's ROC and PR curves showed a hi gh true positive rate for detecting children with two or more ACEs while also po inting to difficulties in classifying single ACE instances accurately."
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