Titlebook: Natural Language Processing in Biomedicine; A Practical Guide Hua Xu,Dina Demner Fushman Textbook 2024 The Editor(s) (if applicable) and Th

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NLP Applications—Clinical Documentsresearch. Finally, we also discuss the growing importance and potential of generative Artificial Intelligence (AI), with particular emphasis on state-of-the-art large?language models like ChatGPT and GPT-4, in using clinical documents for the healthcare applications.

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Question Answeringh that can be answered by consumer-friendly sources; biologists need informative summaries of the recent publications in their areas of interest; administrators have questions about healthcare policies; quality of healthcare, and disease outbreaks. These and many other areas of question answering an

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Large Language Model and Text Generationvarious types of prompts such as cloze prompts, prefix prompts, hard/discrete prompts, and soft/continuous prompts. We also introduce prompt-based learning methods using encoder-only LLMs and decoder-only LLMs. Next, we focus on the ability of generative LLMs for synthetic text generation and introd

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NLP Applications—Biomedical Literatureding text simplification and automatic summarization targeting laypeople. Lastly, we explore the synergistic potential between literature mining and other data modalities. We conclude the chapter by highlighting some of the challenges facing literature mining.

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ten insofern eine Art ?negative Auslese“.. Das Vertrauen in die Politiker ist zumindest gering, mehr noch: Es sank in den letzten Jahren scheinbar unaufhaltsam auf immer neue Tiefstst?nde und droht bereits, das Vertrauen in die demokratischen Institutionen insgesamt zu gef?hrden.