Using Machine Learning with Real-World Data to Identify Autism Risk in Children

项目摘要/摘要 自闭症谱系障碍(ASD)的早期和准确识别非常重要，因为ASD干预 可以支持积极的长期发展结果，但年龄之间有两年的延迟 儿童可以可靠地得到诊断和平均诊断年龄；每4名8岁的美国儿童中就有1名患有自闭症 还没有被诊断出来。女孩和拉丁裔儿童不成比例地受到延误问题的影响 ASD的诊断和识别不足，部分原因是临床医生不太可能认识到ASD的风险 因素，并转介他们进行ASD评估。因此，在人群水平上预测ASD风险是 需要加强早期和准确的检测，特别是在这些服务不足的人群中。研究人员 开始利用临床信息学方法从电子健康的真实数据中识别ASD 使用结构化(例如，诊断代码)和非结构化数据(例如，医生笔记)的记录(EHR)。 然而，现有的算法存在着多个主要缺陷，包括训练的不代表性 样本、过时的诊断代码和自然语言处理(NLP)方法，以及缺乏“验证” ASD诊断在他们的黄金标准数据集中。这项拟议的研究通过开发一种 使用最先进的机器学习和NLP方法的当代ASD风险模型。使用电子病历数据 来自洛杉矶儿童医院(包括一个黄金标准数据集，其中包含来自 Boone Fetter诊所)和One佛罗里达数据信托(佛罗里达州全州范围的EHR数据库)，我们将(1)开发一个 使用结构化和非结构化EHR数据(包括父母报告的)计算ASD的表型 ASD鉴别器和与ASD相关的特征，通常在儿童记录中的自由文本中找到)， (2)建立ASD的机器学习风险预测模型。这将为临床应用奠定基础 决策支持工具，集成到EHR中，在儿童需要进行ASD评估时通知临床医生。 这有可能提高所有儿童对自闭症的认同度，但它可能特别有利于女孩和拉丁裔 儿童，减少性别和种族差异。此外，它将很容易扩展为对 整体PCORnet，为2400多万儿童提供医疗保健。通过使用EHR，这项建议成立 承诺未来具有成本效益的卫生系统干预措施，有助于纠正社会人口 通过接触有自闭症风险的女孩和拉丁裔儿童，使自闭症研究中的“不平衡”。

PROJECT SUMMARY/ABSTRACT Early and accurate identification of autism spectrum disorder (ASD) is important because ASD interventions can support positive long-term developmental outcomes, but there is a delay of >2 years between the age children can reliably be diagnosed and the average age of diagnosis; and 1 in 4 U.S. children aged 8 with ASD have not been diagnosed. Girls and Latino children are disproportionately impacted by the problem of delayed diagnosis and under-identification of ASD, in part because clinicians are less likely to recognize ASD risk factors in them and refer them for an ASD evaluation. Therefore, predicting ASD risk at a population level is needed to enhance early and accurate detection, particularly in these underserved populations. Researchers are beginning to harness clinical informatics methods to identify ASD from real-world data in electronic health records (EHRs), using both structured (e.g., diagnosis codes) and unstructured data (e.g., physician notes). However, existing algorithms suffer from multiple major flaws, including non-representativeness of training samples, outdated diagnosis codes and natural language processing (NLP) methods, and a lack of ‘verified’ ASD diagnosis in their gold standard datasets. This proposed research addresses these gaps by developing a contemporary ASD risk model that uses state-of-the-art machine learning and NLP methods. Using EHR data from Children’s Hospital Los Angeles (including a gold standard dataset with ‘verified’ ASD diagnoses from the Boone Fetter Clinic) and the OneFlorida Data Trust (a Florida state-wide EHR database), we will (1) develop a computable phenotype for ASD using both structured and unstructured EHR data (including parent-reported ASD discriminators and features associated with ASD that are often found in free text in children’s records), and (2) develop a machine-learning risk prediction model for ASD. This will lay the foundation for a clinical decision support tool, to be integrated into EHRs to notify a clinician when a child warrants ASD evaluation. This has potential to improve ASD identification in all children, but it may particularly benefit girls and Latino children, reducing sex and ethnic disparities. Further, it will be easily expandable into a ‘next steps’ study to the overall PCORnet, which provides healthcare to over 24 million children. By using EHRs, this proposal holds promise for future cost-effective health systems interventions that can help to correct a sociodemographic ‘imbalance’ in ASD research by reaching girls and Latino children at risk for ASD.