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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要（请参阅说明）：智能手机和传感器的扩散使人们可以不断监测生理，环境和公共卫生通知。但是，只有在患者环境中处理大量的多感官和多模式观察数据并可以采取行动以实现早期检测，才有个性化的数字健康和赋予患者权能才能成为现实；更好的诊断，有效的预防和治疗疾病；并改善生活质量。这项研究将在哮喘的背景下进行，该哮喘影响3亿人，每年在全球范围内夺取超过25万人的生活。根据疾病预防控制中心（CDC）的数据，美国9.3％的儿童患有哮喘。了解哮喘及其管理受到挑战；这是一种具有主题原因和症状的多因素疾病，可以使用基于证据的行动建议来改善广泛的诊断和治疗指南。随着移动传感器和连接传感器的冒险，有关哮喘触发因素的及时信息已获得，其剥削需要精确理解和量化感知的数据作用，以预防和主动评估哮喘。在由计算机科学家和实用临床领导的该应用程序中，我们解决了从使用KHealth技术监控的数据中得出可行信息的核心问题。通过一项试点研究，我们在临床医生的指导下对几名儿科患者进行了同等的KHealth套件，以进行可靠的数据收集和患者操作，并有望初步结果。我们通过解决语义表示，集成，抽象以及对医学知识中涵盖物理，环境数据和在线数据的异质观察的性格解释的问题来建立基础。我们的主要假设是，基于证据的哮喘方法可以帮助医生更确切地确定哮喘的原因，严重性和控制，并提醒患者寻求及时的临床援助以更好地管理哮喘。