Applying Machine Learning in the Prediction and Identification of Children Affected by Prenatal Alcohol Exposure

项目总结 胎儿酒精谱系障碍(FASD)是由产前酒精暴露引起的，发生在多达5%的 在美国的人口，并与终身残疾有关。在……方面存在多重困难 获得FASD的准确诊断，包括细微的物理特征和异质性 演示文稿。因此，FASD被严重低估，大多数受影响的儿童从未 接受诊断。如果FASD能够更早、更可靠地得到诊断，多年的有益 干预不会失败。 这项研究的目标是将机器学习应用于高维数据中的特征良好的数据 集合来预测或描述患有FASD的儿童的特征。这项研究的中心假设是 与专家临床相比，机器学习的应用将准确地预测和识别FASD 诊断。为了验证这一假设，将使用机器学习来：1)基于 2)建立学龄前儿童FASD的多变量预测因子 儿童，以及3)确定区分酒精相关的诊断特异性神经发育标志物 无产前暴露的神经发育缺陷导致的神经发育缺陷。两个二次数据 这项建议将使用来源；一项对400名孕妇及其子女进行的前瞻性研究 乌克兰(其中一半人大量饮酒)，对FASD进行了全面的临床评估，并进行了交叉- 对美国四个地区2900多名一年级儿童进行了分区研究，所有人都进行了FASD临床评估。 在成功完成拟议的研究后，预期的贡献将更加准确 FASD儿童的预测和再认识。这项拟议的研究具有创新性，因为它代表了 将机器学习技术融入FASD的预测模型，从而背离当前的实践。 作为一名围产期流行病学家，我在分析技术方面有很强的基础，并在 机器学习将进一步增强这些技能。此外，针对疾病的畸形学培训 神经发育将为FASD领域做出重大贡献提供坚实的基础 研究。最后，在编写赠款和负责任地进行研究方面的培训和指导将提供 向独立研究人员过渡的坚实基础。这项拟议的研究建立在先前 NIAAA资助了我的跨学科指导团队的研究，他们都强烈支持这一点 研究和培训计划。 机器学习在FASD研究中的这一开创性应用将展示其预测和 确定受影响的儿童，最终导致对产前接触酒精的儿童进行更早的干预。

Project summary Fetal alcohol spectrum disorders (FASD), which are caused by prenatal alcohol exposure, occur in up to 5% of the population in the United States, and are associated with lifelong disability. There are multiple difficulties in obtaining an accurate diagnosis of FASD, including subtlety of physical features and heterogeneity in presentation. Consequently, FASD is grossly under-recognized, and the majority of affected children never receive a diagnosis. If FASD could be diagnosed earlier and with more reliability, many years of beneficial intervention would not be lost. The objective of this research is to apply machine learning to high-dimensional data in well-characterized data sets to predict or characterize children with FASD. The central hypothesis of this research is that the application of machine learning will accurately predict and recognize FASD compared with expert clinical diagnosis. To test this hypothesis, machine learning will be employed to: 1) characterize FASD based on the presence of non-cardinal malformations, 2) establish multivariate predictors of FASD in preschool aged children, and 3) identify diagnosis specific neurodevelopmental markers that distinguish alcohol related neurodevelopmental deficits from neurodevelopmental deficits without prenatal exposure. Two secondary data sources will be used in this proposal; a prospective study of 400 pregnant women and their offspring in Ukraine (half of whom consumed high amounts of alcohol) with full clinical evaluations for FASD, and a cross- sectional study of over 2,900 first grade children in four regions of the U.S., all with clinical FASD evaluations. Upon successful completion of the proposed research, the expected contribution is for more accurate prediction and recognition of children with FASD. The proposed research is innovative, as it represents a departure from current practice by incorporating machine learning techniques into predictive models of FASD. As a perinatal epidemiologist, I have a strong foundation in analytic techniques, and the advanced training in machine learning will further enhance these skills. Additionally, the disease-focused training in dysmorphology and neurodevelopment will provide a strong foundation to make significant contributions to the field of FASD research. Finally, training and mentoring in grant writing and the responsible conduct of research will provide a strong foundation to transition to an independent researcher. This proposed research builds on previous NIAAA funded research by my interdisciplinary mentoring team, who are all strongly supportive of this research and training plan. This seminal application of machine learning to FASD research will demonstrate its capacity to predict and identify affected children, ultimately leading to earlier intervention of children prenatally exposed to alcohol.