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