SCH: kHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care

SCH：kHealth：个性化哮喘护理的语义多感官移动方法

• 批准号: 9294106
• 负责人: AMIT P SHETH
• 金额: $ 33.87万
• 依托单位: WRIGHT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9294106
• 关键词: Acute; Address; Affect; Air; Albuterol; Algorithms; Ambrosia; Area; Asthma; Big Data; Biological Sciences; Breathing; Caring; Cellular Phone; Centers for Disease Control and Prevention (U.S.); Cereals; Child; Child health care; Childhood; Childhood Asthma; Clinic; Clinical; Clupeidae; Coal; Collaborations; Computers; Consult; Consultations; Coughing; Data; Data Analytics; Data Collection; Data Sources; Decision Making; Detection; Development Plans; Diagnosis; Dimensions; Disease; Doctor of Medicine; Dust; Early Diagnosis; Education; Effectiveness; Entrepreneurship; Environment; Environment and Public Health; Environmental Wind; Ethnic Origin; Evaluation; Event; Exposure to; Faculty; Family; Finding by Cause; Foundations; Future; Genes; Glean; Goals; Guidelines; Health; Health Care Costs; Health Communication; Healthcare; Home environment; Hospitals; Human; Humidity; Hydrocarbons; Hypersensitivity; Individual; Informatics; Inhalators; Instruction; Internet; Intervention; Knowledge; Lead; Learning; Location; Machine Learning; Medical; Medicine; Mission; Modeling; Molds; Monitor; National Heart, Lung, and Blood Institute; National Institute of Child Health and Human Development; Nebulizer; Nitric Oxide; Notification; Office Visits; Ontology; Ozone; Parkinson Disease; Patient Care; Patient Monitoring; Patient Recruitments; Patients; Pediatrics; Perception; Persons; Pharmaceutical Preparations; Phase; Physicians; Physiologic Monitoring; Physiological; Physiology; Pilot Projects; Pollen; Population; Prevention; Preventive; Process; Public Health; Quality of life; Recommendation; Recording of previous events; Regulation; Reporting; Research; Risk; Role; Scientist; Semantics; Serinus; Severities; Signal Transduction; Socioeconomic Status; Source; Students; Symptoms; System; Techniques; Technology; Temperature; Testing; Time; United States Food and Drug Administration; Visit; Weight; Wheezing; Work; abstracting; asthma prevention; asthmatic patient; base; clinical application; computer based Semantic Analysis; cost; data mining; design; digital; empowered; empowerment; evidence base; experience; fall risk; falls; grass pollen; handheld mobile device; health data; health disparity; health literacy; improved; information organization; innovation; insight; interest; medical schools; member; men; mid-career faculty; mobile application; multimodality; multisensory; operation; pediatric patients; prevent; respiratory; sensor; sound; statistics; tree pollen; trend; web services

PROJECT SUMMARY (See instructions): The proliferation of smartphones and sensors has made continuous monitoring of physiology, environment, and public health notifications possible. However, personalized digital health and patient empowerment will become reality only if massive multisensory and multimodal observational data is processed within patient context and made actionable to enable early detection; better diagnosis, effective prevention and treatment of the disease; and improve the quality of life. This research will be performed in the context of asthma, which affects 300 million people and claims over 250,000 lives worldwide annually. According to CDC, 9.3% of US children have asthma. Understanding asthma and its management is challenging; it is a multifactorial disease with subjective causes and symptoms, and extant diagnosis and treatment guidelines can be improved using evidence-based action recommendation. With the advent of mobile and connected sensors, timely information about asthma triggers has become available, and its exploitation requires precisely understanding and quantifying the sensed data's role for preventive and proactive assessment of asthma. In this application led by computer scientists and a practicing clinician, we address the central problem of deriving actionable information from data monitored using kHealth technology. Through a pilot study, we have tested our kHealth kit equipped on several pediatric patients under the guidance of a clinical doctor for reliable data collection and patient operation, with promising initial results. We build on this foundation by addressing the problem of semantic representation, integration, abstraction, and personalized interpretation of heterogeneous observations spanning physiological, environmental data, and online data in the context of medical knowledge. Our primary hypothesis is that an evidence-based approach to asthma can help doctors determine more precisely the cause, severity and control of asthma, and can alert patients to seek timely clinical assistance to better manage asthma.

项目总结（见说明）： 智能手机和传感器的普及使得持续监测生理、环境和公共卫生通知成为可能。然而，只有在患者背景下处理大量多感官和多模式观察数据并使其可操作以实现早期检测，更好地诊断，有效预防和治疗疾病，并提高生活质量，个性化数字健康和患者赋权才能成为现实。这项研究将在哮喘的背景下进行，哮喘每年影响3亿人，并在全球范围内夺去25万多人的生命。根据CDC的数据，9.3%的美国儿童患有哮喘。了解哮喘及其管理具有挑战性;它是一种多因素疾病，具有主观原因和症状，现有的诊断和治疗指南可以使用循证行动建议进行改进。随着移动的和连接的传感器的出现，关于哮喘触发的及时信息已经变得可用，并且其利用需要精确地理解和量化感测数据对于预防和主动评估哮喘的作用。在这个由计算机科学家和执业临床医生领导的应用程序中，我们解决了从使用kHealth技术监测的数据中获取可操作信息的核心问题。通过一项试点研究，我们在临床医生的指导下，在几名儿科患者身上测试了我们配备的kHealth套件，以进行可靠的数据收集和患者操作，并取得了令人满意的初步结果。我们建立在这个基础上，通过解决语义表示，集成，抽象和个性化的解释异构观察跨越生理，环境数据和在线数据的背景下，医学知识的问题。我们的主要假设是，基于证据的哮喘治疗方法可以帮助医生更准确地确定哮喘的病因、严重程度和控制，并提醒患者及时寻求临床帮助，以更好地管理哮喘。