Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"clinical time-series"”.
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Knowledge Distillation for Interpretable Clinical Time Series Outcome Prediction
A common machine learning task in healthcare is to predict a patient’s final outcome given their history of vitals and treatments. For example, sepsis is a life-threatening condition that happens when the body has an extreme response to an infection. Treating sepsis is a complicated process, and we …
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Data-Efficient Machine Learning with Applications to Cardiology
… (2) improving supervised learning on small clinical datasets of electrocardiograms (ECGs), where we develop a new data augmentation strategy for ECGs that helps boost performance on a range of predictive problems; (3) improving pre-training through the use of nested optimization, introducing …
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Towards Precision Oncology: A Predictive and Causal Lens
… how these elements can be woven together into a clinical decision support tool. In this thesis, I explore each of these aspects in turn: i) first, I build different models of clinical time-series data, with a focus on prediction of survival outcomes and forecasting of core biomarkers, ii) next, I …
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Switching State Space Modeling via Constrained Inference for Clinical Outcome Prediction
In clinical settings, timely and accurate prediction of adverse patient outcomes can help guide treatment decisions. While deep learning models such as LSTMs have demonstrated strong predictive performance on multivariate clinical time series, they often lack interpretability. To address this gap, …
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Information and generative deep learning with applications to medical time-series
Physiological time-series data are a valuable but under-utilised resource in intensive care medicine. These data are highly-structured and contain a wealth of information about the patient state, but can be very high-dimensional and difficult to interpret. Understanding temporal relationships …
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Adapting Transformers for Structured Data Domains
… (i) sparse and irregularly sampled multivariate time-series, (ii) general-purpose programming languages, (iii) short text clustering, and (iv) natural language interfaces to relational databases —this dissertation proposes novel domain-specific Transformer based models. For the first domain, we …