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 liña: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 66569 |2 nlm | ||
| 100 | 1 | |a Esmail Zadeh Nojoo Kambar, Mina | |
| 245 | 1 | |a Harnessing NLP and Large Language Models for Pattern Discovery and Information Extraction in Electric Health Reports | |
| 260 | |b ProQuest Dissertations & Theses |c 2024 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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. | |
| 653 | |a Engineering | ||
| 653 | |a Biomedical engineering | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2024) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3109723414/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3109723414/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |