Diagnostics and Prognostics with High Dimensional Spatial-Temporal Data: From Structures to Human Brains
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Abstract
Diagnostics and prognostics with high-dimensional spatial-temporal data require innovative methodologies due to the inherent complexity of such datasets. This thesis explores the challenges of diagnostics and prognostics in high-dimensional spatial-temporal data, extending from physical structures to complex human brain analyses through resting-state functional magnetic resonance imaging (rs-fMRI). Drawing an analogy to engineering structural health monitoring using spatial-temporal vibration data, the approach leverages techniques from engineering diagnostics and prognostics data analytics to handle clinical problems with similar characteristics. A pioneering approach is developed to analyze multimodal datasets that not only include advanced rs-fMRI features—Amplitude of Low-frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), Euler Characteristics (EC), and Fractal Analysis—but also encompass a wide array of clinical data. This integration includes infant developmental metrics such as birth weight and gestational age, maternal health factors like BMI and fat mass, and environmental influences including dietary intake and mental health during pregnancy. The study establishes a robust computational framework that uses advanced machine learning algorithms to analyze the interplay of these diverse data types, enhancing the precision and predictive power of our models for early childhood development. Initial validations have demonstrated the effectiveness of this comprehensive approach in identifying ADHD, with ongoing efforts aimed at expanding the methodology to address a broader range of developmental disorders. This work not only advances the diagnostic and prognostic capabilities in medical imaging but also significantly contributes to the field of Prognostics and Health Management (PHM). By providing a solid foundation for managing and understanding high-dimensional and multimodal spatial-temporal data across various disciplines, it bridges the gap between engineering and clinical diagnostics, demonstrating the potential for cross-disciplinary innovation.
How to Cite
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ADHD, rs-fMRI, CAMEL, diagnostic model, machine learning
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