Global ETD Search
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Showing 1 to 5 of 5 for “"Machine learning for health"”.
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Towards Rigorously Tested & Reliable Machine Learning for Health
When can we rely on machine learning in high-risk domains like healthcare? In the long-term, we want machine learning systems to be as reliable as any FDA-approved medication or diagnostic test. Building reliable models is complicated by the need for causal reasoning and robust performance. To …
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Effective knowledge extraction and knowledge-enhanced machine learning for health
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Leveraging Structure and Knowledge in Clinical and Biomedical Representation Learning
Datasets in the machine learning for health and biomedicine domain are often noisy, irregularly sampled, only sparsely labeled, and small relative to the dimensionality of the both the data and the tasks. These problems motivate the use of representation learning in this domain, which encompasses a …
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Hyperparameters and neural architectures in differentially private deep learning
Using machine learning to improve health care has gained popularity. However, most research in machine learning for health has ignored privacy attacks against the models. Differential privacy (DP) is the state-of-the-art concept for protecting individuals' data from privacy attacks. Using …
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Reliable and decentralised deep learning for physiological data
… bodily functions and processes. By employing machine learning to model these data, especially with the advancement of mobile sensing technologies, it becomes feasible to automatically and continually monitor and diagnose one's health status. This holds considerable promise for easing the …