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: 10245104
• 负责人: Gretchen E. Bandoli
• 金额: $ 17.71万
• 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/10245104
• 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.

项目总结