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 20 of 56 for “"outcome prediction"”.
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NBA Machine Learning for Game Outcome Prediction
… National Basketball Association (NBA) gameoutcomes using only information available prior to tip-off. While team-level rating systems such as Elo provide a strong baseline for game prediction, they do not fully account for game-to-game variation in roster composition, player availability, …
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Outcome prediction and structure discovery in healthcare data
… reduce 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 …
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Investigating EEG burst suppression for coma outcome prediction
… the patient. This project seeks to improve this prediction process by analyzing features of the patients' EEG recordings during coma with the aim to determine quantitative metrics which are predictive of patients' outcome. Specifically, we focus on the analysis of the similarity of bursts during …
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Knowledge Distillation for Interpretable Clinical Time Series Outcome Prediction
… 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 are interested in being …
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G-Network for outcome prediction under dynamic intervention regimes
Counterfactual prediction is useful in settings where one would like to know what would have happened had an alternative regime been followed, but one only knows the outcomes under the observational regime. Typically, the regimes are dynamic and time-varying. In these scenarios, G-computation can …
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Deep Learning and Radiomics Based Outcome Prediction for Cancer Patients
The accurate prediction of cancer patient treatment outcomes is essential for personalized treatment planning and improved treatment outcome. The use of machine learning methods, such as deep learning (DL) and radiomics, has been gaining attention in the field of cancer research for predicting …
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Modelling outcome prediction for trauma patients : an artificial intelligence approach
… used to compare the expected and the observed outcomes inrelation to mortality. Thus, the rate of unexpected deaths or survivals can beexamined and any related problems such as improper trauma patient's care can beidentified. In general, the model with this particular task can be referred to as …
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Study of Human Factors Variables in Battle Outcome Prediction Models
… in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific …
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Outcome prediction in intensive care with special reference to cardiac surgery
… the organ failure score the best predictor of outcome. The TISS score was felt to be more likely to be representative of intensiveness of medical and nursing management than severity of illness. The APACHE II score was already becoming widely used world-wide and although it performed less well …
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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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PATIENT SIMILARITY NETWORKS-BASED METHODS FOR MULTIMODAL DATA INTEGRATION AND CLINICAL OUTCOME PREDICTION
… as patient subtyping and predicting clinical outcomes through clustering and classification techniques. They offer the benefits of being interpretable and privacy-preserving, and are capable of integrating multimodal data. The growing availability of multi-omics data, combined with the …
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A Recurrent Network Approach to G-Computation for Sepsis Outcome Prediction Under Dynamic Treatment Regimes
… and over-resuscitation can lead to adverse outcomes. While many retrospective studies have attempted to understand the relationship between sepsis treatment, fluid overload, mortality, and other outcomes, most are correlation-based and cannot actually estimate the causal effects of …
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Uncertainty Quantification in Deep Learning Models of G-Computation for Outcome Prediction under Dynamic Treatment Regimes
… inference method for making counterfactual predictions and estimating treatment effects under dynamic and time-varying treatment regimes. Two G-Net models have been successfully implemented: one that uses recurrent neural networks (RNNs) as its predictors, and one that uses transformer …
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Scalable Model for Reaction Outcome Prediction and One-step Retrosynthesis with a Graph-to-Sequence Architecture
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILESto-SMILES translation lead to a simple end-to-end …
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Exploring topological data analysis in gene expression data topology-driven biomarker discovery and clinical outcome prediction in oncology
… two key challenges in cancer research: clinical outcome prediction and biomarker discovery. In this study, we employ Weighted Gene Topological Data Analysis (WGTDA) to extract topological features from gene expression data, which serve as prognostic biomarkers for cancer classification, staging, …
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RADIOMICS FOR OUTCOME PREDICTION IN EARLY-STAGE NON-SMALL CELL LUNG CANCER PATIENTS TREATED WITH STEREOTACTIC BODY RADIOTHERAPY (SBRT): METHODOLOGICAL CHALLENGES AND CLINICAL APPLICATIONS
… a perspective of what the future of advanced outcome modeling could be in the upcoming years.
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Improving Treatment of Local Liver Ablation Therapy With Deep Learning and Biomechanical Modeling
… within our treatment planning system, and an outcome prediction model. The liv er model has been used to segment over 1,800 exams in our clinic since 3/23/2021, and our outcome prediction model provides visual interpretations of model decisions. The culmination of this work has enabled our …
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Prediction of outcome after abdominal aortic aneurysm rupture
… the validity of existing tools recommended for outcome prediction after abdominal aortic aneurysm (AAA) rupture and to design and validate a novel risk scoring instrument. It also aims to examine the utility of novel predictive variables. Finally, it examines the functional outcomes achieved by …
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Conversation understanding and realistic artificial crash data generation with deep learning
… understanding includes conversational outcome, formality, and politeness prediction. For conversation outcome prediction, we use recorded audio calls collected from a partnering Fortune 500 firm that captures conversations between inside salespeople and business customers. Analysis of …
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