| Summary: | This research aims to present advanced predictive models that seamlessly integrate multi-omics data (genomics, transcriptomics, proteomics, etc.) with clinical notes. The goal is to significantly improve the accuracy of dis-ease diagnosis, enhance prognosis predictions, and enable personalized treatment strategies. The research approach includes a mixed-methods de-sign, combining quantitative and qualitative methods. It involves data pre-processing techniques to standardize multi-omics data, advanced natural language processing (NLP) for extracting structured insights from clinical notes, and the development of interpretable predictive models. The research also focuses on clinical translation through the creation of user-friendly in-terfaces and adheres to strict ethical and regulatory guidelines to ensure re-sponsible data usage. The research contributes both theoretically and prac-tically to the fields of multi-omics integration, NLP in healthcare, and pre-dictive modeling. Theoretical advancements encompass a deeper under-standing of data integration, NLP techniques, and model transparency in healthcare. Practically, this research offers prospects for more accurate dis-ease diagnosis, enhanced prognostication, and personalized treatment strat-egies, thereby improving clinical workflows and ensuring ethical data prac-tices.
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