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Design and Development of the Urban Population Health Observatory to Improve Disease Surveillance and Response

Date Issued
May 1, 2022
Author(s)
Brakefield, Whitney
Advisor(s)
Arash Shaban-Nejad
Additional Advisor(s)
Russell Zaretzki, Kristina Kintziger, Haileab Hilafu
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/28451
Abstract

Chronic and infectious diseases have a profound impact on the quality and length of life of populations that suffer from these conditions. Scientists, physicians, and health officials are seeking innovative approaches to decrease the morbidity and mortality of deadly diseases. Incorporating artificial intelligence and data science techniques across the health science domain could improve disease surveillance, intervention planning, and policymaking. In this dissertation, we describe the design and development of the Urban Population Health Observatory (UPHO), an explainable knowledge-based multimodal big data analytics platform. A common challenge for conducting multimodal big data analytics is integrating multidimensional heterogeneous data sources, which often exist in different levels of granularity. These sources range from individual-level to group-level data, as well as structured and unstructured data sources. The UPHO platform is designed to foster the integration of multimodal big data including social determinants of health, observation of daily living, and population health data consistently across jurisdictions to estimate the incidence and prevalence of different health conditions, as well as related risk factors. Leveraging semantics, the UPHO platform provides contextual knowledge from relevant domains of interest by reusing several ontologies focusing on clinical and public health (diseases, geography, etc.). UPHO encompasses an Explainable Artificial Intelligence (XAI) and Interpretability feature which increases trust and trustworthiness, justifies actions and decisions, improves usability, aids in locating sources of error, and can minimize the chance for human error. Developing a multimodal scalable surveillance system to monitor and detect trends as well as deliver rapid early warnings and recommendations could assist health officials, physicians, and researchers in responding, and mitigating a public health crisis.

Subjects

Big Data Analytics

Multimodality

COVID-19

Disease Surveillance

Urban Health

Smart Cities

Disciplines
Diseases
Engineering
Health and Medical Administration
Health Information Technology
Life Sciences
Medicine and Health Sciences
Physical Sciences and Mathematics
Public Health
Social and Behavioral Sciences
Degree
Doctor of Philosophy
Major
Data Science and Engineering
File(s)
Thumbnail Image
Name

DissertationTRACE5.docx

Size

5.06 MB

Format

Microsoft Word XML

Checksum (MD5)

4658705bb37c1588500593bc13e647a3

Thumbnail Image
Name

auto_convert.pdf

Size

2.97 MB

Format

Adobe PDF

Checksum (MD5)

54e7397f28cddb025be1a7a8fde7c8ee

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