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  1. Statistical and algorithmic approaches for health policy and fairness

    Chin, Elizabeth T.
    [Stanford, California] : [Stanford University], 2022

    Advances in statistics, econometrics, and computer science have the potential to facilitate data-driven decision making in improving the health of populations. However, adapting modern data science methods to eliminate health disparities remains challenging because interventions based singularly on health data do not fully address health issues borne from structural, upstream inequities. A multi-level approach that integrates social and health data to characterize how specific social systems perpetuate health inequities provides opportunities to create more tailored health and social policies. I will discuss examples of addressing health inequity through data science in three contexts: (1) mass incarceration in relationship to public health policies, (2) equity for structurally vulnerable populations in public health and social policies, and (3) methods for "small data" in precision health. An underlying theme is the importance of statistical methodology and study design informed by a holistic understanding of the interplay between social and health systems

  2. Addressing data scarcity in humanitarian health and environmental justice

    Huynh, Benjamin Quoc
    [Stanford, California] : [Stanford University], 2022

    Modern computational approaches promise to address issues of health equity through identifying potential disparities or interventions. However, such approaches are subject to data scarcity: structurally marginalized populations tend to have poorer-quality data, and computational tools reliant on high-quality data may exacerbate existing inequities. Through examples in humanitarian, occupational, and environmental health, we examine data science approaches to circumvent data scarcity. We investigate the use of machine learning to model forced migration in humanitarian settings, microsimulation models for high-risk occupational health contexts, and systems-level public health risk estimation for an impending environmental catastrophe. Taken together, this body of work demonstrates how unconventional data sources, novel approaches, and rigorous study design can be employed to advance health equity and environmental justice in the absence of high-quality data

  3. Deep learning tools to accelerate knee osteoarthritis research

    Thomas, Kevin Andrew, 1991-
    [Stanford, California] : [Stanford University], 2021

    Knee osteoarthritis (OA) is a debilitating disease that involves inflammation and degradation of the structures of the knee. It affects over 250 million people globally and has no cure. Research to improve our understanding of this condition and to develop better treatments requires objective measures of disease severity. Radiography and magnetic resonance imaging (MRI) enable visualization of the knee's structures and play an important role in measuring joint health and OA status. However, evaluation of these images by physicians and researchers is prone to subjectivity and requires significant time and expertise, creating a bottleneck that hinders research progress. Given the importance of medical image assessments for OA research, automated tools that mitigate human bias and costs are needed. Deep learning algorithms are capable of training neural network models to automate many medical image analysis tasks. This dissertation describes the development, validation, and deployment of neural networks to automate the assessment of OA in X-rays and MRIs. One of the most commonly used evaluations of OA severity is the Kellgren-Lawrence (KL) score, a semi-quantitative 0-4 score of OA progression based on X-ray findings. We developed a model to automate the staging of OA severity from X-rays using the KL scoring system and compared its performance to that of musculoskeletal radiologists. The model was evaluated on 4,090 images staged by a radiologist committee. Saliency maps were generated to reveal features used by the model to determine KL grades. The model agreed with the consensus of a musculoskeletal radiologist committee as closely as individual musculoskeletal radiologists agreed with the committee. It takes full radiographs as input and predicts KL scores with state-of-the-art accuracy and does not require manual preprocessing. Saliency maps suggested the model's predictions were based on clinically relevant information. Compositional MRI sequences are designed to measure properties of the cartilage microstructure that undergo change with OA. Spin-spin T2 mapping is one of the most widely used compositional MRI techniques for this purpose. We developed a fully-automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin echo (MESE) MRI. Subregional T2 values and four-year changes were calculated using a musculoskeletal radiologist's segmentations and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert for comparison. Assessments of cartilage health using the model's segmentations agreed with those of an expert as closely as experts agreed with one another. We have made both our KL model and T2 mapping model publicly available. The KL model is available at https://github.com/stanfordnmbl/kneenet-docker and the T2 mapping model is available at https://github.com/kathoma/AutomaticKneeMRISegmentation. We have also deployed them as user-friendly web applications at http://kl.stanford.edu to enable their use by the OA research community. This has the potential to accelerate osteoarthritis research

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