Machine Learning Methods to Develop and Deploy Real-Time Risk Surveillance for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder from the Electronic Health Record

自闭症谱系障碍(ASD)和注意力缺陷多动障碍(ADHD)的早期治疗可以 改善长期结果，但只有在及时和准确地识别高危儿童的情况下。早期风险 这两种情况的共同因素(如早产、围产期并发症)在例行程序中收集 护理，并可在电子健康记录(EHR)中访问，但尚未大规模用于风险分层或 提高早期筛查水平。拟议研究的中心前提是《电子健康记录》包含 有助于在发育早期识别有自闭症和多动症风险的儿童的信息，但 将这些信息转化为临床可操作的措施需要新的预测性建模方法。 我们最近由NIMH支持的研究初步支持了这一假设。我们发现， 后来被诊断为自闭症和/或多动症的儿童具有独特的早期卫生系统利用模式，以及 在1岁时获得的EHR数据已经包含了预测未来ASD风险的信息。以此为基础 工作中，拟议的研究将开发和部署ASD和ADHD风险预测模型，该模型可以 直接为临床决策提供信息。首先，我们建议开发方法和预测模型，以 将优化预测性能、缓解已知的偏倚来源，并智能监控模型预测 通过平衡早期识别的好处和等待更多数据的价值来实现风险。第二，我们 建议采取关键步骤，通过前瞻性地评估这些模型，将它们部署到 杜克大学卫生系统(DUHS)EHR，并与提供商合作开发和评估 原型EHR仪表板，显示ASD和ADHD的风险和预计的随访时间，以告知临床 决策。职业发展目标将允许应聘者发展所需的专业知识 一个独立的、跨学科的儿科心理健康机器学习研究项目。职业生涯 培训将与拟议的研究紧密结合，并强调部署、评估和 实施自闭症和多动症的风险预测模型。在拟议的研究中，DUHS EHR数据将是 通过高质量、经验性验证的数据管道访问所有2006年10月1日之后出生的儿童。数据 内容将包括诊断和程序代码、实验室措施和生命体征、临床记录和遇到 细节。诊断将使用之前在DUHS验证的基于ICD-10的标准进行识别。这项研究将 首先创建一个安全的、定期更新的诊断风险预测数据库，以促进 过渡到EHR部署。活动将集中在1-2年的方法和模型开发上，随后 从第三年开始开发、部署和评估风险预测仪表板。此研究 将与探索新的ASD筛查方法和模型指导的同时和后续工作相吻合 DUHS的干预措施，包括以患者和提供者为中心的早期干预策略。建议数 职业发展将支持PI向独立和杜克大学健康数据科学倡议的过渡。

Early treatment of autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) can improve long-term outcomes, but only when at-risk children are identified promptly and accurately. Early risk factors common to both conditions (e.g. premature birth, perinatal complications) are collected during routine care and are accessible in the electronic health record (EHR), but have not been used at scale to stratify risk or improve early screening. The central premise of the proposed research is that the EHR contains information that can help identify children at risk for ASD and ADHD early in development, but translating this information into clinically actionable measures requires new predictive modeling methods. Our recent NIMH-supported research provides initial support for this premise. We have discovered that children later diagnosed with ASD and/or ADHD have distinctive patterns of early health system utilization, and that EHR data acquired by age 1 already contains information predictive of later ASD risk. Building on this work, the proposed research will develop and deploy ASD and ADHD risk prediction models that can directly inform clinical decision-making. First, we propose to develop methods and prediction models that will optimize prediction performance, mitigate known sources of bias, and intelligently surveil model-predicted risk by balancing benefits of earlier identification against the value of waiting for more data. Second, we propose to take critical steps toward clinical use by prospectively evaluating these models, deploying them in the Duke University Health System (DUHS) EHR, and working with providers to develop and evaluate a prototype EHR dashboard that presents ASD and ADHD risk and projected follow-up length to inform clinical decision-making. Career development goals will allow the candidate to develop expertise needed to establish an independent, transdisciplinary research program in machine learning for pediatric mental health. Career training will be tightly integrated with the proposed research and emphasize deployment, evaluation, and implementation of risk prediction models for ASD and ADHD. In the proposed study, DUHS EHR data will be accessed through a high-quality, empirically validated data pipeline for all children born after 10/1/2006. Data elements will include diagnosis and procedure codes, lab measures and vitals, clinical notes, and encounter details. Diagnoses will be identified using ICD-10-based criteria previously validated at DUHS. The study will begin with creation of a secure, regularly updated diagnosis risk prediction database to facilitate a smooth transition to EHR deployment. Activities will focus on methods and model development in years 1-2 followed by development, deployment, and evaluation of the risk prediction dashboard beginning in year 3. This research will dovetail with concurrent and subsequent work exploring novel ASD screening methods and model-guided interventions at DUHS, including patient- and provider-centered early intervention strategies. The proposed career development will support the PI’s transition to independence and health data science initiatives at Duke.