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.
Results
Showing 1 to 20 of 30 for “"Mortality prediction"”.
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Sequential modeling for mortality prediction in the ICU
… scores operate in part by predicting patient mortality in the ICU using physiological variables including lab values, vital signs, and admission information. However, existing evidence suggests that current mortality predictors are less performant on patients who have an especially high risk …
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Risk assessment and mortality prediction in patients with venous thromboembolism using big data and machine learning.
… and it is associated with high morbidity and mortality. Some patients need immediate treatment and monitoring in intensive care units (ICU). Moreover, cancer patients are at increased risk of developing VTE, especially in the immediate period after ICU hospitalization. It is crucial to predict …
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Localized customized mortality prediction modeling for patients with acute kidney injury admitted to the intensive care unit
Introduction. Models for mortality prediction are traditionally developed from prospective multi-center observational studies involving a heterogeneous group of patients to optimize external validity. We hypothesize that local customized modeling using retrospective data from a homogeneous subset …
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Risk stratification in cardiac surgery: Algorithms and applications
… to their validity to predict 30-day and one-year mortality after open-heart surgery, to evaluate if the preoperative risk stratification model EuroSCORE predicts the different components of resource utilization in cardiac surgery, and to systematically evaluate the accuracy and performance of …
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Predicting mortality for patients in critical care : a univariate flagging approach
… including trends in key variables, can improve predictions of patient prognosis, this problem is challenging as the number of variables that must be considered is large and increasingly complex modeling techniques are required. The objective of this thesis is to build a mortality prediction …
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On Dynamic Treatment Regimes: Collaborative Search and LLM-Driven Decision Trees
… This work contains extensive experiments on mortality prediction, time series forecasting, and synthetic patient modeling. Experiments show that vital-based representations do not capture enough meaningful data about a patient to accurately predict and evaluate new treatment methods. By …
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Generalizable neural network representations of patient state in the intensive care unit
… and administration of interventions. These predictions can be made directly on the raw patient data extracted from electronic health records. However, this data can be high dimensional with extraneous information. Neural networks, and in particular, autoencoders and sequence-to-sequence …
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Aetiology and outcome of patients burned from 2003 to 2008 at the Tygerberg Hospital burns unit, Western Cape, SA
… burns in the Western Cape, South Africa. The prediction of outcome in severe burns is important to aid in clinical decision making, improve scarce resource allocation and allow comparisons between different burn units. Age, burn size and the presence of inhalational injury have been determined …
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Quantifying Discard Mortality of Undersized and Ovigerous Crabs in the Gulf of Mexico Blue Crab Fishery
… of undersized and ovigerous blue crabs, yet mortality of these discarded crabs is an understudied component of total mortality. The first objective of this project was to quantify discard mortality of non-target crabs using bycatch surveys across the Lake Pontchartrain–Mississippi Sound …
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Detecting hazardous intensive care patient episodes using real-time mortality models
… strong discrimination ability for patient mortality, with an ROC area (AUC) of 0.880. The final model includes a number of variables known to be associated with mortality, but also computationally intensive variables absent in other severity scores. In addition to RAS, I also develop …
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Bayesian Neural Networks for Actuarial Mortality Modelling
The use of Bayesian neural networks (BNNs) for mortality modelling is an understudied, yet potentially promising area of research. They inherently offer robust uncertainty quantification, and are known for their application to sparse or small datasets. This research investigates the efficacy of BNN …
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Representation Learning for Patients in the Intensive Care Unit
… upon the state-of-the-art in length of stay prediction (with additional investigations into mortality prediction). In Chapter 4, I am again inspired by knowledge of the clinical decision making process to propose a method using graph neural networks to leverage data from similar patients when …
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Relaxing assumptions in deep probabilistic modelling
… performance on clinical objectives such as mortality prediction.
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The burden of trauma in a regional trauma centre in the Western Province of Saudi Arabia – a descriptive study
… descriptive statistics were calculated. Trauma mortality was compared with trauma scores with Receiver Operator Curves. Results: During the study period, 8793 patients were evaluated, 5846 (66.5%) males. The mean age was 27.5 years. 5608 (64%) were admitted in one of the in-hospital departments …
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Adapting Transformers for Structured Data Domains
… attention formulations, auxiliary tasks, prediction layers and loss functions - and adapt them to better suit the structure and semantics of specific data domains. Focusing on four structured domains - (i) sparse and irregularly sampled multivariate time-series, (ii) general-purpose …
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Using Deep Learning to predict the mortality of Leukemia patients
… medicine is now being applied towards the prediction of mortality in childhood Acute Lymphoblastic Leukemia (ALL) patients. This is because individual children differ in the sensitivity of their leukemic cells and in their response to treatment-related toxicity. Currently, mortality …
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Development of a mortality risk prediction score for patients with AML requiring critical care
… with acute myeloid leukemia (AML) have high mortality after intensive care unit (ICU) admission, long-term survival is possible. There is no accepted model for predicting longer-term mortality after ICU admission. We examined the role of five AML and ICU related risk factors in mortality …
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Methodology Development for Improving the Performance of Critical Classification Applications
… for healthcare applications. Numerous prediction applications are developed to predict patients' health conditions. These are critical applications where misdiagnosis can cause serious harm to patients, even death. Due to the imbalanced nature of many clinical datasets, our work …
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Incorporating Climate Sensitivity for Eastern United States Tree Species into the Forest Vegetation Simulator
… over the southern United States, however its prediction accuracy was challenged due to its climate- insensitive nature. The goal of this study was to develop species-specific prediction models for eastern U.S. forest tree species with climate and soil properties as predictors in order to …
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Outcome prediction and structure discovery in healthcare data
… healthcare costs. We first develop an outcome prediction algorithm that preserves the clinical knowledge from the development of additive risk scores with hard thresholds (of the form add p points if variable x is above/below threshold t). This novel method is not only easily optimizable for …
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