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
基本信息
- 批准号:10609515
- 负责人:
- 金额:$ 37.93万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:8 year oldAddressAffectAgeAlgorithmsAutism DiagnosisAwarenessBehaviorBehavioralBeliefCaringCenters for Disease Control and Prevention (U.S.)ChildClassificationClinicClinicalDataDecision MakingDevelopmentDevelopmental DisabilitiesDiagnosisDiagnostic and Statistical Manual of Mental DisordersDiseaseEarly DiagnosisEarly InterventionEarly identificationEarly treatmentEducationElectronic Health RecordEvaluationGoalsHospitalsHumanIndividualIntuitionLabelMachine LearningMedicalMedical EducationModelingMonitorNatural Language ProcessingNeurobiologyOutcomePatternPerformancePhysiciansPrevalenceProductivityRecordsResourcesRiskRisk AssessmentSET DomainSpecialistStructureTechniquesTestingTextTrainingUpdateUse of New TechniquesVocabularyWorkadult with autism spectrum disorderalgorithmic biasautism spectrum disorderbehavioral phenotypingblinddeep learningdevelopmental diseasediagnostic criteriadisorder riskeconomic costelectronic structureexperiencehealth information technologyhigh riskhuman-in-the-loopimprovedimproved outcomelarge datasetsmachine learning algorithmmachine learning modelprototyperandomized trialsocialstructured datatoolunstructured data
项目摘要
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.
项目概要/摘要
自闭症谱系障碍 (ASD) 是一种发育障碍,美国每 54 名儿童中就有 1 人患有自闭症谱系障碍 (ASD) (1)。这
在美国,自闭症谱系障碍 (ASD) 造成的经济损失估计为每年 660 亿美元,其中包括医疗保健和失去父母
生产力 (2)。早期诊断至关重要,因为它可以实现早期治疗和最佳的长期结果。
然而,由于缺乏专家,在早期识别自闭症谱系障碍高风险儿童具有挑战性。到
为了解决这个问题,该项目的目标是利用信息创建健康信息技术(HIT)
电子健康记录 (EHR),以支持非专业临床医生识别自闭症谱系障碍 (ASD) 高风险儿童。
HIT 将集成两个提供补充信息的组件。第一个组件将
利用机器学习算法来标记自闭症高风险儿童的电子病历。既传统又深刻
学习可能相互利用,将在系统跟踪质量和数量的同时进行评估
电子病历中的信息及其对绩效的影响。第二部分将重点关注 EHR 自由文本
并识别诊断标准中定义的表型行为表达
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,将调查并纠正潜在的算法偏差或
当这不可能时记录下来。 HIT 将通过直观的方式结合两个组件的结果
用户界面。由于它的目的是用作人在循环决策工具,因此它还将提供
两个组件性能的描述性数据。最终的 HIT 将使用快速原型开发
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。
该评估将比较识别有 ASD 风险的儿童的准确性、置信度和效率。
并且没有非 ASD 专家的 HIT。它还将系统地关注类型、数量、质量和
所提供信息的透明度,以及这如何与用户对其自身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。
所有组件均已完成前期工作,并取得良好效果。然而,这项先前的工作重点
在 DSM IV 版上,仅使用数据丰富的 EHR 中的自由文本。拟议项目将扩大
之前的工作是使用 DSM-5 标准,训练和开发算法以在中使用结构化和非结构化字段
临床、代表性 EHR,并与不同医院的 EHR 合作,评估潜在障碍和
数据可变性的优点。
利用 EHR 中的信息,该 HIT 将特别为非专业临床医生的评估提供支持
可能有 ASD 风险的儿童。 HIT 将支持早期转诊,从而实现早期诊断和治疗。
它将在缺乏领域专业知识的各种不同环境中发挥作用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('GONDY LEROY', 18)}}的其他基金
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:
10297910 - 财政年份:2021
- 资助金额:
$ 37.93万 - 项目类别:
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 - 财政年份:2021
- 资助金额:
$ 37.93万 - 项目类别:
Audio Generation and Optimization from Existing Resources for Patient Education
利用现有资源生成和优化患者教育音频
- 批准号:
10439893 - 财政年份:2015
- 资助金额:
$ 37.93万 - 项目类别:
Audio Generation and Optimization from Existing Resources for Patient Education
利用现有资源生成和优化患者教育音频
- 批准号:
10295641 - 财政年份:2015
- 资助金额:
$ 37.93万 - 项目类别:
Audio Generation and Optimization from Existing Resources for Patient Education
利用现有资源生成和优化患者教育音频
- 批准号:
10580849 - 财政年份:2015
- 资助金额:
$ 37.93万 - 项目类别:
Large-scale evaluation of text features affecting perceived and actual text diffi
影响感知和实际文本差异的文本特征的大规模评估
- 批准号:
8240419 - 财政年份:2011
- 资助金额:
$ 37.93万 - 项目类别:
Large-scale evaluation of text features affecting perceived and actual text diffi
影响感知和实际文本差异的文本特征的大规模评估
- 批准号:
8714350 - 财政年份:2011
- 资助金额:
$ 37.93万 - 项目类别:
Large-scale evaluation of text features affecting perceived and actual text diffi
影响感知和实际文本差异的文本特征的大规模评估
- 批准号:
8018414 - 财政年份:2011
- 资助金额:
$ 37.93万 - 项目类别:
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