Health Information Technology to Support Autism Spectrum Disorders (ASD) Risk Assessment for Early Diagnosis

项目摘要/摘要 自闭症谱系障碍(ASD)是一种发育障碍，在美国每54个儿童中就有一个受到影响(1)。这个 在美国，自闭症的经济成本估计为每年660亿美元，其中包括医疗保健和失去父母 生产力(2)。早期诊断是至关重要的，因为它允许早期治疗和最佳的长期结果。 然而，由于缺乏专家，在早期确定ASD的高危儿童是具有挑战性的。至 为了解决这个问题，该项目的目标是使用信息创建健康信息技术(HIT 在电子健康记录(EHR)中，支持非专家临床医生识别患有自闭症高危儿童。 HIT将整合两个提供互补信息的组件。第一个组件将 利用机器学习算法来标记自闭症高危儿童的EHR。既有传统的，也有深刻的 在系统跟踪质量和数量的同时，将对潜在的相互影响的学习进行评估 电子病历中的信息及其对绩效的影响。第二个组件将侧重于电子病历自由文本 并确定《诊断和统计》中定义的诊断标准的表型行为表达 精神障碍手册(DSM)。基于规则的自然语言处理将与机器相结合 学习算法。对于这两个组件，将调查并纠正潜在的算法偏差或 记录了无法执行此操作的情况。Hit将通过直观的 用户界面。由于它旨在用作人在回路中的决策工具，因此它还将提供 关于两个组件的性能的描述性数据。最终的成功将使用快速原型技术进行开发 与领域专家互动。它将在具有代表性的非专家临床医生的用户研究中进行评估。 该评估将比较识别ASD高危儿童的准确性、置信度和效率与 而且没有非自闭症专家的打击。它还将系统地关注类型、数量、质量和 提供的信息的透明度，以及这与用户对自己专业知识的信念如何相互作用 因为他们偏向于机器决策。不同类型的EHR以及不同水平的临床专业知识 将对使用Hit的效果进行比较。 对所有部件进行了初步工作，取得了良好的效果。然而，这项先前的工作主要集中在 在DSM的第四版上，并且只使用来自数据丰富的EHR的自由文本。拟议的项目将扩大 之前的工作是使用DSM-5标准，培训和开发算法，以在 临床、有代表性的电子病历，并与不同医院的电子病历一起评估潜在的障碍和 数据可变性的优势。 利用EHR中的信息，该HIT将特别为非专家临床医生提供支持，以评估他们的 可能有自闭症风险的儿童。HIT将支持早期转诊，从而导致早期诊断和治疗。 在缺乏领域专业知识的各种不同环境中，它将是有用的。

Project Summary / Abstract Autism spectrum disorder (ASD) is a developmental disorder that affects 1 in 54 children in the US (1). The economic cost of ASD is estimated to be $66 billion per year in the US, from medical care and lost parental productivity (2). Early diagnosis is crucial since it allows for early treatment and the best long-term outcome. However, identifying children at high risk for ASD at an early age is challenging due to lack of specialists. To address this problem, the project's objective is to create health information technology (HIT) using information in electronic health records (EHR) to support non-expert clinicians in identifying children at high risk for ASD. The HIT will integrate two components that provide complementary information. The first component will leverage machine learning algorithms to label EHR of children at high risk for autism. Both traditional and deep learning, potentially leveraging each other, will be evaluated while systematically tracking quality and quantity of information in EHR and their effect on performance. The second component will focus on the EHR free text and identify phenotypic behavioral expressions of diagnostic criteria as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Rule-based natural language processing will be combined with machine learning algorithms. For both components, potential algorithm bias will be investigated and corrected or documented when this is not possible. The HIT will combine results from both components through an intuitive user interface. Since it is intended to be used as a human-in-the loop decision tool, it will also provide descriptive data on performance for both components. The final HIT will be developed using rapid prototyping in interaction with domain experts. It will be evaluated in a user study with representative non-expert clinicians. The evaluation will compare accuracy, confidence, and efficiency of identifying children at risk for ASD with and without the HIT by non-ASD experts. It will also systematically focus on the type, amount, quality and transparency of information provided, and how this interacts with user beliefs about their own expertise as well as their bias toward machine decisions. Different types of EHR as well as different levels of clinical expertise will be compared for effects of HIT use. Preliminary work has been conducted for all components with good results. However, this prior work focused on version IV of the DSM and used only free text from data rich EHR. The proposed project will expand the prior work to use DSM-5 criteria, train and develop the algorithms to use structured and unstructured fields in clinical, representative EHR, and work with EHR from different hospitals to evaluate potential obstacles and advantages of variability in data. Using information in EHR, this HIT will provide support especially for non-expert clinicians in their evaluation of children who may be at risk of ASD. The HIT will support early referrals leading to early diagnosis and therapy. It will be useful in a variety of different settings where domain expertise is missing.