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Data Analytics and Decision Making : = Evaluating Risk and Burden Associated with Infectious Respiratory Diseases.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Analytics and Decision Making :/
其他題名:	Evaluating Risk and Burden Associated with Infectious Respiratory Diseases.
作者:	Mele, Jessica A.
面頁冊數:	1 online resource (174 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-06, Section: B.
Contained By:	Dissertations Abstracts International84-06B.
標題:	Infections. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30167937click for full text (PQDT)
ISBN:	9798358417625

Data Analytics and Decision Making : = Evaluating Risk and Burden Associated with Infectious Respiratory Diseases.
Mele, Jessica A.

Data Analytics and Decision Making :Evaluating Risk and Burden Associated with Infectious Respiratory Diseases. - 1 online resource (174 pages)

Source: Dissertations Abstracts International, Volume: 84-06, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2022.

Includes bibliographical references

Infectious respiratory diseases cause substantial burden on healthcare systems in the United States each year. In recent years, two of the most notable infectious respiratory diseases have been pneumonia and COVID-19. Recent studies suggest that pneumonia causes 1.5 million unique hospitalizations in the United States each year whereby COVID-19 is estimated to have caused 4.6 million hospitalizations nationally since it first emerged in 2020. In addition to the increased risk of severe disease and mortality, pneumonia and COVID-19 both incur substantial healthcareassociated costs. Understanding risk factors and disease characteristics remain critical components to improving health outcomes and reducing overall burden associated with these two diseases.This work explores the use of data analytics to develop tools to overcome modern challenges surrounding computational modeling and big data in healthcare with the intention to provide interpretable solutions to inform public health. The proposed models are developed to be used as tools to improve population health and facilitate intervention design to reduce severe outcomes for pneumonia and COVID-19. This work proposes new ideas and developments to guide public health policy and draws insights that result from applying these methods to real-world data.The ideas presented in this work expand upon current literature surrounding infectious respiratory disease risk, prevention, and prevalence, and provides tools to: (i) identify at-risk populations; (ii) recommend targeted intervention policies; and (iii) evaluate and understand disease burden. First, risk will be explored via the development of three predictive models to understand and evaluate 30-day risk factors for pneumonia hospitalization at the individual, community, and provider levels. Next, data-driven models will be employed to provide recommendations for targeted intervention policies to reduce hospitalizations and healthcare-associated costs. Lastly, disease progression models will be developed to inform population susceptibility, understand historical risk, and evaluate mitigation tactics associated with COVID-19.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798358417625Subjects--Topical Terms:

1621997
Infections.
Index Terms--Genre/Form:

542853
Electronic books.

Data Analytics and Decision Making : = Evaluating Risk and Burden Associated with Infectious Respiratory Diseases.
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520 $a Infectious respiratory diseases cause substantial burden on healthcare systems in the United States each year. In recent years, two of the most notable infectious respiratory diseases have been pneumonia and COVID-19. Recent studies suggest that pneumonia causes 1.5 million unique hospitalizations in the United States each year whereby COVID-19 is estimated to have caused 4.6 million hospitalizations nationally since it first emerged in 2020. In addition to the increased risk of severe disease and mortality, pneumonia and COVID-19 both incur substantial healthcareassociated costs. Understanding risk factors and disease characteristics remain critical components to improving health outcomes and reducing overall burden associated with these two diseases.This work explores the use of data analytics to develop tools to overcome modern challenges surrounding computational modeling and big data in healthcare with the intention to provide interpretable solutions to inform public health. The proposed models are developed to be used as tools to improve population health and facilitate intervention design to reduce severe outcomes for pneumonia and COVID-19. This work proposes new ideas and developments to guide public health policy and draws insights that result from applying these methods to real-world data.The ideas presented in this work expand upon current literature surrounding infectious respiratory disease risk, prevention, and prevalence, and provides tools to: (i) identify at-risk populations; (ii) recommend targeted intervention policies; and (iii) evaluate and understand disease burden. First, risk will be explored via the development of three predictive models to understand and evaluate 30-day risk factors for pneumonia hospitalization at the individual, community, and provider levels. Next, data-driven models will be employed to provide recommendations for targeted intervention policies to reduce hospitalizations and healthcare-associated costs. Lastly, disease progression models will be developed to inform population susceptibility, understand historical risk, and evaluate mitigation tactics associated with COVID-19.
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