[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124976-en":3,"doc-seo-124976-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124976,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Advances in Prior-Driven Machine Learning Techniques for Health Applications with Limited Data","Machine learning in healthcare faces obstacles such as limited access to large retrospective datasets from regulatory constraints and the cost, time, and delays of prospective data collection through clinical trials. This dissertation leverages prior domain knowledge, using physical and physiological information to improve data efficiency across three healthcare applications. It introduces a model-based hybrid feature extraction approach for blood pressure waveform analysis, a deep harmonic prior model for separating mixed signals from optical biosensors, and a deep prior ensemble learning method that boosts separation performance using limited experimental data from large pregnant animal models. Validation on clinical and preclinical data shows substantial improvements over prior work, including robustness to waveform deformations from aging and vascular stiffness, and better time-frequency source separation for wearable biosensors.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nAdvances in Prior-Driven Machine Learning Techniques for Health Applications with Limited Data  \nPermalink  \n[https://escholarship.org/uc/item/8mh6q189](https://escholarship.org/uc/item/8mh6q189)  \nAuthor  \nSaffarpour, Mahya  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nAdvances in Prior-Driven Machine Learning Techniques for Health Applications with Limited Data  \nBy  \nMAHYA SAFFARPOUR  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY  \nin  \nComputer Engineering  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \n\n| Soheil Ghiasi, Chair |\n| --- |\n| Chen-Nee Chuah |\n\nLifeng Lai Committee in Charge  \n2024  \nAbstract  \nAdoption of machine learning (ML) techniques in healthcare applications is hindered by several challenges, including difficulty in accessing large volumes of retrospective data due to regulatory constraints, or the cost and delay associated with acquiring prospective data through clinical experiments. We observe that prior domain knowledge can enable efficient use of the limited quantity of data, and develop techniques that leverage prior physical and physiological information to enhance ML data efficiency for three illustrative applications arising in healthcare. Specifically, we present a model-based hybrid feature extraction method for improved analysis of blood pressure waveforms; a deep harmonic prior model for separation of mixed signals sensed by optical biosensors; and a deep prior ensemble learning method for boosting signal separation performance for experimental data acquired in large pregnant animal models. The proposed methodologies are validated with relevant clinical or preclinical data, demonstrating significant effectiveness compared to previous work.  \nThe model-based approach combines a simplified cardiovascular model with a rule-based method to augment data, and to extract key features from arterial blood pressure waveforms, significantly enhancing feature detection accuracy in real data. It offers a data-efficient technique for feature extraction in circulatory system studies, offering robustness in face of blood pressure waveform deformations due to pathological conditions, such as aging and vascular stiffness.  \nWe then present a custom deep learning model for separation of quasi-periodic physiological signals in wearable biosensors, integrating harmonic priors within a neural structure to efficiently separate signal sources in time-frequency images using limited data. This approach is validated using both synthetic and in vivo data, showing substantial improvements over existing signal separation techniques. Extending the deep harmonic prior technique, we propose a deep prior ensemble learning method, a boosting approach that optimizes learning bias and variance to enhance the accuracy and stability of signal separation in challenging real-world conditions. The method is  \ndeveloped to address the unique requirement of separating low-SNR biological signals, and its effectiveness is demonstrated using photoplethysmography (PPG) signals that are non-invasively acquired from pregnant ewe models.  \nThe thesis bridges the gap between theoretical advancements in machine learning and practical requirements of a set of healthcare applications, demonstrating that generalizable machine learning performance can be effectively achieved in data-restricted environments, thus showcasing the potential for adoption in a broader set of healthcare applications.  \nContents  \n1 Introduction 1  \n1.1 Significance ......................................... 2  \n1.2 Thesis Overview ...................................... 3  \n1.2.1 Chapter 2: Model-Based Learning Approach for Circulatory System Analysis 3  \n1.2.2 Chapter 3: Adap","cbCaip4UOti6WclY","https://ap.wps.com/l/cbCaip4UOti6WclY","pdf",53961383,1,126,"English","en",105,"# Introduction\n## Significance\n## Thesis Overview\n## Chapter 2: Model-Based Learning Approach for Circulatory System Analysis\n## Chapter 3: Adaptive Deep Prior Embedding for Data-Efficient Signal Separation in Wearable Biosensors\n## Chapter 4: Boosted Deep Harmonic Priors for Robust and High-Precision Biosensors’ Signal Separation\n# Model-Based Learning Approach for Circulatory System Analysis\n## Physiological Background\n## Physiowise: A Physics-Aware Approach to Dicrotic Notch Identification\n## Results: Physiowise\n## Modeling Acute Hemorrhage Management: A Systemic Prior-Informed Parameter Learning Methodology","[{\"question\":\"为什么医疗场景的机器学习常受限于数据？\",\"answer\":\"医疗应用常难以获取大规模回顾性数据，受到监管限制；同时前瞻性临床实验的数据采集也伴随较高成本与时间延迟。\"},{\"question\":\"本论文如何利用先验知识提升数据效率？\",\"answer\":\"论文将物理与生理信息作为先验，设计模型与深度学习方法，在数据受限条件下提升特征提取与信号分离效果。\"},{\"question\":\"论文提出的三类方法分别解决哪些医疗数据任务？\",\"answer\":\"方法包括：用于血压波形分析的模型化混合特征提取、用于光学生物传感器混合信号分离的深谐波先验模型、以及用于可穿戴生物传感器信号分离的深先验集成学习（boosting）方法。\"}]","Advances in Prior-Driven 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