Harnessing NLP and Large Language Models for Pattern Discovery and Information Extraction in Electric Health Reports
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| Publicado en: | ProQuest Dissertations and Theses (2024) |
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ProQuest Dissertations & Theses
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | In this work, we report on a series of natural language processing tools and models to improve the efficiency and accuracy of information discovery from clinical trials and pharmacological studies. Our main contributions are: 1. The development of an open-source platform Tri-AL that• Enables dynamic tracking of clinical trials information over time,• Excels in data visualization and user interaction with a particular emphasis on enhancing the analysis and representation of race and ethnicity data to foster equity in clinical research, and• Includes a predictive model utilizing machine learning to decipher drug mechanisms of action.2. Heterogeneous Graph Neural Network for Gene-Chemical Entity Relation Extraction: We created a supervised deep learning model that adapts a heterogeneous Graph Neural Network to extract gene-chemical components. This model augments word representations using message passing that accurately identifies gene-chemical named entities and their relationships class.3. Bipartite Graph Model for Evaluating Summarization Performance: We proposed a bipartite graph model to evaluate the performance of large language models in summarizing clinical trials. This model provides a robust framework to assess the accuracy and effectiveness of automated summarization tools in the medical domain. |
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| ISBN: | 9798384437598 |
| Fuente: | ProQuest Dissertations & Theses Global |