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Contemporaneous Health Monitoring and Biomarker Discovery by Integration of Patient Data and Disease Knowledge
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Contemporaneous Health Monitoring and Biomarker Discovery by Integration of Patient Data and Disease Knowledge. |
| 개인저자 | Samareh Abolhasani, Banafsheh. |
| 단체저자명 | University of Washington. Industrial and Systems Engineering. |
| 발행사항 | [S.l.]: University of Washington., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 136 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-05B. Dissertation Abstract International |
| ISBN | 9781088374429 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Advisor: Huang, Shuai. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Technological innovations have given rise to data-rich environments that support the use of heterogeneous sensor measurements to monitor complex healthcare systems. Despite these advancements, however, there remains little understanding of how patient health evolves in real clinical settings and how changes in health condition generate manifestations that are captured by the data. To address this knowledge gap, my research aims to build disease trajectory modeling that can reconstruct the evolving patient's health condition over time, termed the contemporaneous health index (CHI), by combining data and the natural history model of the disease. This global index may help increase the continuity of care, facilitate patient-provider communication, and assist with a range of clinical decision makings. However, lack of deep understanding of the disease and its progression, existence of patient heterogeneity, inherent uncertainty in predictive models and the emergence of large amounts of complex, and unstructured data, all could compromise prediction capabilities.In this dissertation, we developed innovative methodologies that reflect the progression of the underlying patient condition by learning personalized models, quantifying the uncertainty of those models, and creating effective biomarker engineering pipelines to analyze large amounts of complex data for effective incorporation into the calculation of CHI. We first, proposed a novel clinical data fusion framework, named DL-CHI: a dictionary learning-based CHI that quantifies the severity of the deterioration process over time and represents monotonic progression patterns with a systematic optimization formulation. DL-CHI mitigated the heterogeneity of the patients by incorporating dictionary learning to create personalized models for individual patients. We then developed the UQ-CHI framework: an uncertainty quantification-based model of CHI to further enhance the disease trajectory modeling with uncertainty quantification by considering imperfect and continuous delivery of knowledge via probabilistic nature of maximum entropy discrimination (MED) principle. Finally, we proposed effective biomarker engineering pipelines to enable possible extensions of the CHI for building trajectory models from complex data (video, audio, text, and mobile sensorreading data).We applied the proposed methodologies to real-world applications, including Alzheimer's disease (AD), surgical site infection (SSI), depression and human activity recognition using wearable sensors. |
| 일반주제명 | Industrial engineering. Biomedical engineering. Health sciences. Bioinformatics. Epidemiology. Electrical engineering. |
| 언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
