هندسة تكنولوجيا المعلومات

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Browsing هندسة تكنولوجيا المعلومات by Author "رشا زكي مصطفى عساف"

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    Modeling Adverse Events Detection as Time Series Using LSTM and Labeled Data Fusion
    (Al-Quds Univeersity, 2026-05-27) Rasha Zaki Mustafa Assaf; رشا زكي مصطفى عساف
    Adverse events (AEs) , characterized as undesirable and unintentional outcomes of medical treatments, pose significant challenges in healthcare. Accurate prediction of these events is crucial for enhancing patient safety, optimizing resource allocation, and improving overall health- care outcomes. Traditional methods, such as statistical analyses and early machine learning techniques, often fail to capture complex, nonlinear relationships of medical data. This dissertation explores the application of advanced machine learning models, particularly Long Short- Term Memory (LSTM ) and transformers, to predict adverse events and outcome using Food and Drug Administration (FDA ) Adverse Event Reporting System ( FAERS), a database containing information on drug safety, and Medical Entities in Digital Records Annotated ( Med- DRA) datasets. The dissertation focuses on integrating these comprehensive datasets, followed by data cleaning and pre-processing to ensure data accuracy and reliability. Latent Dirichlet Al- location( LDA) and clustering, a statistical method for topic modeling was used to address the complexity of classifying over 50,000 adverse event categories, reducing the number of classes to 10. The study further investigates the efficacy of sequence-to-sequence models, such as the Transformer, in predicting adverse events. Results indicate that Transformer-based models out- perform traditional machine learning algorithms in predicting adverse events, demonstrating improved accuracy and robustness. The sequence-to-sequence models achieved an accuracy of 92.81%, with a precision of 93.22%, a recall of 91.89%, and an F1 score of 92.55%. The integration of self-attention mechanisms enhances these models by allowing them to capture complex patterns and relationships within the data—insights that traditional approaches often overlook s. We implemented a Graph Neural Network ( GNN) and evaluated precision and re- call across multiple categories, yielding low overall F1 scores of 0.36 for term type classification and 0.42 for FAERS reaction classification.