Solida tabellar 
 
 
 
 
 
 Abstract and Introduction 
 
 
 
 
 
 
 
 
 
 System Architecture 2.1  Access via UI or HTTP 2.1.1  GUI 2.2  Input 2.3  Natural Language Processing Pipeline — The Llettuce API 2.3.1  Vector search 2.3.2  LLM 2.3.3  Concept Matches 2.4  Output 
 
 
 
 
 
 
 
 
 Case Study: Medication Dataset 3.1  Data Description 3.2  Experimental Design 3.3  Results 3.3.1  Comparison between vector search and Usagi 3.3.2  Comparison with GPT-3 3.4  Conclusions & Acknowledgement 3.5  References 2. Architektonik sistem Llettuce Python-da yazılmışdır. back-end o‘z simantic search, lokal LLM to suggest mappings, and queries to a connected OMOP CDM database to provide the details of a suggested mapping to the user. 2.1 UI o‘z HTTP Llettuce o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: Pipeline opsiyalar opsiyalardir, o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z list o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. 2.1.1 GUI Bu, Streamlit (Streamlit, 2024) Python frameworkga qurulub, o‘z iki opsiyalladi. First, textual box bo‘ladi ki, user o‘z bir komma-diyaradi listga informal adlar qilmadi. Second, o‘z qilmadi qilmidi qilmidi qilmidi. 2.2 Gilmadi Llettuce o‘z FastAPI (Ramírez, 2024) endpoints o‘z API endpoints. O‘z endpoints o‘z Llettuce modules o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z. 2.3 Natural Language Processing Pipeline — Llettuce API 2.3.1 Vector search Omop CDM o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z 2.3.2 LLM Llettuce Llama.cpp (Gerganov et al., 2024) kullani Llama LLM (Dubey et al., 2024). Llama.cpp transformer inference ko‘radi API. O‘z hardwar detektar, o‘z optimizations for efficient inference running on central processing units (CPUs). Llama modelni 8 bilyon parameterni (Llama 3.1 8B). Modellar 32-bit flocking point numardani. Kvantitasiyalar modelini qilmadi, o‘z az qilmadi (Dettmers & Zettlemoyer, 2023; Jacob et al., 2017). Fully accurate Llama 3.1 8B o‘z 32 GB RAM qilmadi, o‘z 4 bit qilmidi modelini 5 GB qilmadi. Llettuce Haystack (deepset GmbH, 2024) kullani LLM pipelinesini orchestrating. LLM o‘z pipelinesni o‘z component o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘ U o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z You are an assistant that suggests formal RxNorm names for a medication. You will be given the name of a medication, along with some possibly related RxNorm terms. If you do not think these terms are related, ignore them when making your suggestion. Respond only with the formal name of the medication, without any extra explanation. O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z O‘z LLM o‘z bir formalaq ad qoysadi, o‘z formalaq ad qoysadi, bu formalaq ad o‘z parameterized OMOP CDM query. 2.3.3 Concept Matches Omop CDM queries generates using an object-relational mapping through SQLAlchemy (Bayer, 2012). Konsepti name field-da kulluni string o‘z qilmadi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi, o‘z qilmidi. 2.4 Kilmadi Llettuce pipelines JSON o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z: {"madi" : ["Madi'nin o‘zingizini qilmadi]} JSON output va "events" llm_output and omop_output for each name sent to Llettuce. Llm_output o‘z LLM’nin qaytarini, informal_namni qilmadi, meta meta describing metadata o‘z LLM’nin qilmadi. Omop_output event o‘z omop-CDM, o‘z CONCEPT array, o‘z o‘z o‘z o‘z omop_output event, o‘z o‘z omop_output event, o‘z o‘z o‘z omop-CDM, o‘z o‘z CONCEPT array, o‘z o‘z o‘z o‘z o‘z o‘z concept_name, concept_id, vocabulary_id, and concept_code of the OMOP-CDM’s concept table, o‘z o‘z concept_name_similarity_score calculated in Llettuce. GUI o‘z bilan o‘z o‘z o‘z daha user-friendly format. 
 
 
 
 
 
 
 
 
 
 
 
 
 Autorlar : (1) James Mitchell-White, Centre for Health Informatics, School of Medicine, The University of Nottingham, Digital Research Service, The University of Nottingham, NIHR Nottingham Biomedical Research Centre; (2) Reza Omdivar, Digital Research Service, University of Nottingham, NIHR Nottingham Biomedical Research Center; (3) Esmond Urwin, Center for Health Informatics, School of Medicine, University of Nottingham, NIHR Nottingham Biomedical Research Center; (4) Karthikeyan Sivakumar, Digital Research Service, University of Nottingham; Ruizhe Li, NIHR Nottingham Biomedical Research Centre and School of Computer Science, University of Nottingham; (6) Andy Rae, Center for Health Informatics, School of Medicine, University of Nottingham; (7) Xiaoyan Wang, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapur; Theresia Mina, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapur; (9) John Chambers, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapur and Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK; (10) Grazziela Figueredo, Center for Health Informatics, School of Medicine, University of Nottingham and NIHR Nottingham Biomedical Research Center; Philip R Quinlan, Center for Health Informatics, School of Medicine, University of Nottingham. Authors: (1) James Mitchell-White, Centre for Health Informatics, School of Medicine, The University of Nottingham, Digital Research Service, The University of Nottingham, NIHR Nottingham Biomedical Research Centre; (2) Reza Omdivar, Digital Research Service, University of Nottingham, NIHR Nottingham Biomedical Research Center; (3) Esmond Urwin, Center for Health Informatics, School of Medicine, University of Nottingham, NIHR Nottingham Biomedical Research Center; (4) Karthikeyan Sivakumar, Digital Research Service, University of Nottingham; Ruizhe Li, NIHR Nottingham Biomedical Research Centre and School of Computer Science, University of Nottingham; (6) Andy Rae, Center for Health Informatics, School of Medicine, University of Nottingham; (7) Xiaoyan Wang, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapur; Theresia Mina, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapur; (9) John Chambers, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapur and Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK; (10) Grazziela Figueredo, Center for Health Informatics, School of Medicine, University of Nottingham and NIHR Nottingham Biomedical Research Center; Philip R Quinlan, Center for Health Informatics, School of Medicine, University of Nottingham. 
 
 Bu dokimi CC BY 4.0 lisenziyada arxiv qaytaradi. Bu dokimi CC BY 4.0 lisenziyada arxiv qaytaradi.

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