Exploring Strength in Weakness: Multifaceted Weakly Supervised Learning Approaches for Telehealth and Industrial Quality Predictions
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Alenezi, Dhari F.
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Abstract
The rapid advancement in data collection technologies has significantly impacted the fields of telehealth and industrial manufacturing. However, the challenge of obtaining comprehensive and precise labels for this data poses significant difficulties for traditional machine learning models. This thesis addresses these challenges by proposing three novel weakly supervised learning (WSL) approaches, specifically targeting the domains of telehealth and manufacturing quality prediction.
The first study introduces a Ranking-Based Weakly Supervised Learning (RWSL) model for assessing disease severity in telemonitoring, with a focus on Parkinson’s disease. Using data from the mPower app, this model integrates both labeled and ranked samples to improve predictive accuracy, overcoming the challenge of limited labeled data. By leveraging weak supervision, the RWSL model provides a more accurate and timely assessment of disease progression.
The second study presents a Physics-Informed Weakly Supervised Learning (PWL) framework designed for quality prediction in industrial manufacturing processes. This approach integrates the physics-based understanding of manufacturing processes with machine learning models, improving prediction accuracy despite the scarcity of labeled samples. The PWL model bridges the gap between theoretical physics-based models and practical machine learning applications, enabling more effective quality control in manufacturing.
The third study proposes a Multi-source Multi-task Weakly Supervised Transfer Learning (M2WeST) approach for telehealth, which addresses the heterogeneity of disease manifestations across patients. This model combines data from multiple patients, incorporating both strong and weak labels to provide personalized disease severity predictions. The M2WeST framework significantly improves the robustness and accuracy of predictions, even in scenarios with limited labeled data.
These three studies contribute to the advancement of weakly supervised learning in both telehealth and industrial applications, offering innovative solutions for extracting meaningful insights from weakly labeled data.
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2024-12-02
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Dissertation