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

应用机器学习来预测和识别受产前酒精暴露影响的儿童

• 批准号: 10475144
• 负责人: Gretchen E. Bandoli
• 金额: $ 17.45万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10475144
• 关键词: Affect; Alcohols; Algorithms; Attention deficit hyperactivity disorder; Behavior; Behavioral; Characteristics; Child; Clinical; Complex; Consumption; Cross-Sectional Studies; Data; Data Set; Data Sources; Development; Diagnosis; Disease; Dysmorphology; Early Diagnosis; Early Intervention; Early identification; Epidemiologist; Epidemiology; Evaluation; Fetal Alcohol Exposure; Fetal Alcohol Spectrum Disorder; Foundations; Funding; Future; Goals; Grant; Growth; Heterogeneity; Infant; Intervention; Lead; Logistic Regressions; Machine Learning; Measures; Mentors; Mentorship; Methodology; Modeling; National Institute on Alcohol Abuse and Alcoholism; Neurodevelopmental Deficit; Neuropsychology; Nursery Schools; Nutritional; Outcome; Pattern; Perinatal; Population; Pregnancy; Pregnant Women; Process; Prospective Studies; Provider; Research; Research Personnel; Research Training; Sampling; Seminal; Techniques; Testing; Training; Ukraine; United States; Writing; accurate diagnosis; aged; alcohol consumption during pregnancy; alcohol exposure; autism spectrum disorder; base; clinical diagnosis; disability; fetal diagnosis; first grade; improved; infancy; innovation; machine learning algorithm; malformation; maternal alcohol use; multidimensional data; neurodevelopment; novel strategies; offspring; predictive modeling; prenatal; prenatal exposure; research clinical testing; responsible research conduct; skills; sociodemographics; success

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.

项目摘要 胎儿酒精谱系障碍（FASD）是由产前酒精暴露引起的， 在美国的人口，并与终身残疾。有多重困难， 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可以更早诊断，更可靠， 干预不会失败。 本研究的目的是将机器学习应用于特征数据中的高维数据 用于预测或描述FASD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预测和识别FASD 诊断.为了检验这一假设，将采用机器学习来：1）基于FASD的特征， 存在非基数畸形，2）建立学龄前儿童FASD的多变量预测因子 儿童，和3）确定诊断特异性神经发育标志物，区分酒精相关 神经发育缺陷与产前未暴露的神经发育缺陷。二次数据 来源将用于这项建议; 400名孕妇及其后代的前瞻性研究， 乌克兰（其中一半人饮酒量高），对FASD进行了全面的临床评估， 对美国四个地区2,900多名一年级儿童的横断面研究，都有FASD临床评估 一旦成功完成拟议的研究，预期的贡献是更准确的 FASD儿童的预测和识别。这项研究是创新的，因为它代表了一个 通过将机器学习技术结合到FASD的预测模型中来偏离当前的实践。 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， 机器学习将进一步增强这些技能。此外，以疾病为重点的畸形学培训 和神经发育将为FASD领域做出重大贡献提供坚实的基础 research.最后，培训和指导在赠款写作和负责任的行为研究将提供 为过渡到独立研究人员打下坚实的基础。这项研究建立在以前的基础上。 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早期干预。