Machine-Assisted Interdisciplinary Approach For Early Clinical Evaluation of Neurodevelopmental Disorders

摘要 神经发育迟缓是大多数罕见疾病的特征，通常是第一个症状。 罕见疾病的非特定早期表现对患者和照顾者都构成了挑战，他们经常为 多年没有确诊的疾病，以及必须区分常见疾病和罕见疾病的医生。 早期评估可以简化诊断流程，并导致快速实施有针对性的 治疗。在这项提案中，我们的主要目标是缩短获得综合基因的途径 通过初级保健电子医疗对可疑神经发育障碍(NDDS)的评估 基于记录(EMR)的机器学习算法识别临床符合遗传条件的患者 评估。我们讨论了在初级保健环境中整合测试前遗传咨询的计划，通过 视频和远程医疗，并将开发一种可适应儿科初级保健的范例 工作流程。我们将在UG3阶段通过一个 学术遗传学家、神经发育儿科医生和初级保健之间的密切合作 华盛顿儿童健康中心(CHC)的儿科医生，并在 UH3适用于CNH Goldberg中心的所有业务。因此，我们将把早期的遗传评估带到最大 通过利用我们的多学科团队，在华盛顿大都会地区建立初级儿科诊所网络 致力于早期识别和表征非处方药物。我们将致力于实现以下目标： 目标1(UG3)：评估可扩展的机器辅助管道在早期识别患者方面的应用 利用基于EMR的自动特征提取的NDDS。我们将训练并反复改进一台机器- 基于EMR识别遗传性NDD高危儿童的学习算法。 目标2(UG3)：评估初级保健临床医生发起的多学科评估的有效性，以加快 遗传评估和神经发育表型。我们的工作流程从自动图表开始 身份识别将允许初级保健提供者访问我们的多学科神经发育遗传学 一队。包括远程医疗、基于应用的视频和电子摄像在内的技术创新将 促进这一进程。 目标3(UH3)：通过以下方法评估机器辅助识别EMR中NDDS的普适性 扩大进入戈德堡中心儿科诊所的整个网络。我们将扩展到所有CNH 初级保健诊所服务于高度多样化的华盛顿特区大都市区，并确保方法是 对不同地点的特定人口和流行病学因素具有很强的适应能力。 我们的方法将在初级保健开始时识别出发育迟缓的患者 诊断奥德赛，加快深入的表型和基因调查，以及重新评估 对不同DC大都市人群进行早期诊断的测序结果。

ABSTRACT Neurodevelopmental delay is a feature of a majority of rare diseases and is often the first presenting sign. Nonspecific early presentations of rare disorders challenge both patients and caregivers who often struggle for years without diagnoses, and physicians who must distinguish between common concerns and rare disease. Early evaluations can streamline the diagnostic process and lead to rapid implementation of targeted therapies. In this proposal, our primary objective is to shorten the pathway to comprehensive genetic evaluations for suspected neurodevelopmental disorders (NDDs) through primary care electronic medical record (EMR) based machine-learning algorithmic identification of patients clinically eligible for genetic evaluation. We discuss our plan for integration of pretest genetic counseling in the primary care setting through video and telemedicine, and will develop a paradigm that can be adapted to the pediatric primary care workflow. We will implement and iteratively improve upon our algorithms during the UG3 Phase through a close partnership between academic geneticists, neurodevelopmental pediatricians, and the primary care pediatricians of Children’s Health Center (CHC) in Washington DC, and transition the mature program during the UH3 to all CNH Goldberg Center practices. We will thus bring early genetic evaluations to the largest network of primary pediatric practices in the D.C. Metropolitan area by leveraging our multidisciplinary team dedicated to early identification and characterization of NDDs. We will address the following aims: Aim 1 (UG3): Assess utility of a scalable machine-assisted pipeline for early identification of patients with NDDs based on automated feature extraction from EMR. We will train and iteratively refine a machine- learning algorithm to identify children at high risk of genetic NDDs based on their EMR. Aim 2 (UG3): Assess utility of a primary care clinician-initiated multidisciplinary evaluation to expedite genetic evaluation and neurodevelopmental phenotyping. Our workflow starting with automated chart identification will permit primary care providers access to our multidisciplinary neuro-developmental-genetics team. Technical innovations including telemedicine, application based videos, and electronic intakes will facilitate this process. Aim 3 (UH3): Evaluate generalizability of machine-assisted identification of NDDs from EMR by expanding access to entire network of Goldberg Center Pediatric practices. We will expand to all CNH primary care clinics serving the highly diverse Washington DC metropolitan area and ensure approach is robust to the specific demographic and epidemiologic factors of different sites. Our approach will identify patients with developmental delay in the primary care setting at the beginning of a diagnostic odyssey and expedite deep phenotyping and genetic investigations, as well as reevaluate sequencing results for early diagnosis in the diverse DC metropolitan population.