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

健康信息技术支持自闭症谱系障碍 (ASD) 风险评估及早期诊断

• 批准号: 10458014
• 负责人: GONDY LEROY
• 金额: $ 31.65万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10458014
• 关键词: 8 year old; Address; Affect; Age; Algorithms; Autism Diagnosis; Awareness; Behavior; Behavioral; Belief; Caring; Centers for Disease Control and Prevention (U.S.); Child; Classification; Clinic; Clinical; DSM-IV; Data; Decision Making; Development; Developmental Disabilities; Diagnosis; Diagnostic and Statistical Manual of Mental Disorders; Early Diagnosis; Early Intervention; Early identification; Early treatment; Electronic Health Record; Evaluation; Goals; Hospitals; Human; Individual; Intuition; Label; Machine Learning; Medical; Medical Education; Modeling; Monitor; Natural Language Processing; Neurobiology; Outcome; Pattern; Performance; Physicians; Prevalence; Productivity; Records; Resources; Risk; Risk Assessment; Specialist; Structure; Techniques; Testing; Text; Training; Update; Use of New Techniques; Vocabulary; Work; adult with autism spectrum disorder; algorithmic bias; autism spectrum disorder; base; behavioral phenotyping; blind; deep learning; developmental disease; diagnostic algorithm; diagnostic criteria; disorder risk; economic cost; electronic structure; experience; health information technology; high risk; human-in-the-loop; improved; improved outcome; large datasets; machine learning algorithm; machine learning model; prototype; randomized trial; social; structured data; tool; unstructured data

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.

项目摘要 /摘要 自闭症谱系障碍（ASD）是一种发育障碍，影响美国54名儿童中有1个（1）。这 在美国，ASD的经济成本估计每年为每年660亿美元 生产力（2）。早期诊断至关重要，因为它允许早期治疗和最佳的长期结局。 但是，由于缺乏专家，确定在早期ASD高风险的儿童是具有挑战性的。到 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）中支持非专业临床医生确定ASD高风险的儿童。 HIT将集成两个提供互补信息的组件。第一个组件将 利用机器学习算法将儿童的EHR标记为自闭症的高风险。传统和深 在系统地跟踪质量和数量的同时，将评估学习，可能相互利用的学习 EHR中的信息及其对性能的影响。第二个组件将重点放在EHR免费文本上 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结合 学习算法。对于这两个组件，将研究和纠正潜在算法偏差或 记录在不可能的情况下。命中将通过直觉结合两个组件的结果 用户界面。由于它旨在用作循环决策工具，因此也将提供 有关两个组件性能的描述性数据。最终的命中将使用快速原型开发 与领域专家的互动。它将在用户研究中与代表性的非专业临床医生进行评估。 评估将比较确定ASD风险的儿童的准确性，信心和效率 而且没有非ASD专家的命中。它还将系统地关注类型，数量，质量和 提供的信息的透明度，以及如何与用户对自己专业知识的信念相互作用 作为他们对机器决策的偏见。不同类型的EHR以及不同水平的临床专业知识 将比较HIT使用的效果。 已经针对所有成分进行了良好结果的初步工作。但是，这项先前的工作集中 在DSM的IV版本上，仅使用来自Data Rich EHR的免费文本。拟议的项目将扩大 使用DSM-5标准的事先工作，训练和开发算法，以使用结构化和非结构化字段 临床，代表EHR，并与来自不同医院的EHR合作，以评估潜在的障碍和 数据可变性的优势。 使用EHR中的信息，此命中将为非专家临床医生提供支持 可能有ASD风险的孩子。命中将支持早期推荐，导致早期诊断和治疗。 它在缺少域专业知识的各种不同的环境中很有用。