{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T03:03:02Z","timestamp":1780110182734,"version":"3.54.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T00:00:00Z","timestamp":1740009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001777","name":"The University of Wollongong","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001777","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Healthc Inform Res"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Information extraction (IE) of unstructured electronic health records is challenging due to the semantic complexity of textual data. Generative large language models (LLMs) offer promising solutions to address this challenge. However, identifying the best training methods to adapt LLMs for IE in residential aged care settings remains underexplored. This research addresses this challenge by evaluating the effects of zero-shot and few-shot learning, both with and without parameter-efficient fine-tuning (PEFT) and retrieval-augmented generation (RAG) using Llama 3.1-8B. The study performed named entity recognition (NER) to nursing notes from Australian aged care facilities (RACFs), focusing on agitation in dementia and malnutrition risk factors. Performance evaluation includes accuracy, macro-averaged precision, recall, and F1 score. We used non-parametric statistical methods to compare if the differences were statistically significant. Results show that zero-shot and few-shot learning, whether combined with PEFT or RAG, achieve comparable performance across the clinical domains when the same prompting template is used. Few-shot learning significantly outperforms zero-shot learning when neither PEFT nor RAG is applied. Notably, PEFT significantly improves model performance in both zero-shot and few-shot learning; however, RAG significantly improves performance only in few-shot learning. After PEFT, the performance of zero-shot learning reaches a comparable level with few-shot learning. However, few-shot learning with RAG significantly outperforms zero-shot learning with RAG. We also found a similar level of performance between few-shot learning with RAG and zero-shot learning with PEFT. These findings provide valuable insights for researchers, practitioners, and stakeholders to optimize the use of generative LLMs in clinical IE.<\/jats:p>","DOI":"10.1007\/s41666-025-00190-z","type":"journal-article","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T16:56:09Z","timestamp":1740070569000},"page":"191-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Adapting Generative Large Language Models for Information Extraction from Unstructured Electronic Health Records in Residential Aged Care: A Comparative Analysis of Training Approaches"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5851-7158","authenticated-orcid":false,"given":"Dinithi","family":"Vithanage","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1147-5741","authenticated-orcid":false,"given":"Chao","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0961-0441","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0212-4598","authenticated-orcid":false,"given":"Mengyang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Alkhalaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1853-4978","authenticated-orcid":false,"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2786-0775","authenticated-orcid":false,"given":"Yunshu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7910-9396","authenticated-orcid":false,"given":"Ping","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,20]]},"reference":[{"key":"190_CR1","unstructured":"Hu Y et al (2024) \"Information extraction from clinical notes: are we ready to switch to large language models?,\" arXiv preprint arXiv:2411.10020"},{"issue":"2","key":"190_CR2","doi-asserted-by":"publisher","DOI":"10.2196\/12239","volume":"7","author":"S Sheikhalishahi","year":"2019","unstructured":"Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V (2019) Natural language processing of clinical notes on chronic diseases: systematic review. JMIR Med Inform 7(2):e12239","journal-title":"JMIR Med Inform"},{"key":"190_CR3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jbi.2017.07.012","volume":"73","author":"K Kreimeyer","year":"2017","unstructured":"Kreimeyer K et al (2017) Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 73:14\u201329","journal-title":"J Biomed Inform"},{"key":"190_CR4","unstructured":"Hussain M (2022) \"Knowledge extraction from unstructured clinical text using active transfer learning approach,\" 2022."},{"issue":"01","key":"190_CR5","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1055\/s-0038-1638592","volume":"17","author":"SM Meystre","year":"2008","unstructured":"Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF (2008) Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 17(01):128\u2013144","journal-title":"Yearb Med Inform"},{"key":"190_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.cosrev.2018.06.001","volume":"29","author":"A Goyal","year":"2018","unstructured":"Goyal A, Gupta V, Kumar M (2018) Recent named entity recognition and classification techniques: a systematic review. Computer Science Review 29:21\u201343","journal-title":"Computer Science Review"},{"key":"190_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jvcir.2019.02.001","volume":"60","author":"X Liu","year":"2019","unstructured":"Liu X, Zhou Y, Wang Z (2019) Recognition and extraction of named entities in online medical diagnosis data based on a deep neural network. J Vis Commun Image Represent 60:1\u201315","journal-title":"J Vis Commun Image Represent"},{"key":"190_CR8","doi-asserted-by":"crossref","unstructured":"Vithanage D, Zhu Y, Zhang Z, Deng C, Yin M, and Yu P (2024) \"Extracting symptoms of agitation in dementia from free-text nursing notes using advanced natural language processing,\" in MEDINFO 2023\u2014The Future Is Accessible: IOS Press, 700\u2013704.","DOI":"10.3233\/SHTI231055"},{"issue":"1","key":"190_CR9","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1186\/s12911-024-02557-5","volume":"24","author":"H Muizelaar","year":"2024","unstructured":"Muizelaar H, Haas M, van Dortmont K, van der Putten P, Spruit M (2024) Extracting patient lifestyle characteristics from Dutch clinical text with BERT models. BMC Med Inform Decis Mak 24(1):151","journal-title":"BMC Med Inform Decis Mak"},{"key":"190_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102822","volume":"150","author":"Z Gu","year":"2024","unstructured":"Gu Z et al (2024) Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model. Artif Intell Med 150:102822","journal-title":"Artif Intell Med"},{"key":"190_CR11","doi-asserted-by":"crossref","unstructured":"Gao Y, Mahajan D, Uzuner \u00d6, and Yetisgen M (2024) \"Clinical natural language processing for secondary uses,\" vol. 150, ed: Elsevier, p. 104596.","DOI":"10.1016\/j.jbi.2024.104596"},{"key":"190_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104559","volume":"149","author":"Z Cui","year":"2024","unstructured":"Cui Z, Yu K, Yuan Z, Dong X, Luo W (2024) Language inference-based learning for low-resource Chinese clinical named entity recognition using language model. J Biomed Inform 149:104559","journal-title":"J Biomed Inform"},{"issue":"6","key":"190_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3653304","volume":"18","author":"J Yang","year":"2024","unstructured":"Yang J et al (2024) Harnessing the power of llms in practice: a survey on chatgpt and beyond. ACM Trans Knowl Discov Data 18(6):1\u201332","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"1","key":"190_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-023-04203-7","volume":"27","author":"D Myers","year":"2024","unstructured":"Myers D et al (2024) Foundation and large language models: fundamentals, challenges, opportunities, and social impacts. Clust Comput 27(1):1\u201326","journal-title":"Clust Comput"},{"key":"190_CR15","doi-asserted-by":"crossref","unstructured":"Ge J, Li M, Delk MB, and Lai JC (2024) \"A comparison of a large language model vs manual chart review for the extraction of data elements from the electronic health record,\" Gastroenterology, vol. 166, no. 4, pp. 707\u2013709. e3","DOI":"10.1053\/j.gastro.2023.12.019"},{"key":"190_CR16","unstructured":"Chen Q, et al (2023) \"Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations,\" arXiv e-prints, p. arXiv: 2305.16326"},{"issue":"1","key":"190_CR17","first-page":"1","volume":"40","author":"R Zhang","year":"2021","unstructured":"Zhang R, Guo J, Chen L, Fan Y, Cheng X (2021) A review on question generation from natural language text. ACM Transactions on Information Systems (TOIS) 40(1):1\u201343","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"issue":"20","key":"190_CR18","doi-asserted-by":"publisher","first-page":"2776","DOI":"10.3390\/healthcare11202776","volume":"11","author":"P Yu","year":"2023","unstructured":"Yu P, Xu H, Hu X, Deng C (2023) Leveraging generative AI and large language models: a comprehensive roadmap for healthcare integration. Healthcare 11(20):2776","journal-title":"Healthcare"},{"key":"190_CR19","doi-asserted-by":"crossref","unstructured":"Hu Y, et al (2024) \"Improving large language models for clinical named entity recognition via prompt engineering. J Am Med Info Assoc. ocad259","DOI":"10.1093\/jamia\/ocad259"},{"key":"190_CR20","doi-asserted-by":"crossref","unstructured":"Bhate NJ, Mittal A, He Z, and Luo X (2023) \"Zero-shot learning with minimum instruction to extract social determinants and family history from clinical notes using GPT model,\" in 2023 IEEE International Conference on Big Data (BigData), 2023: IEEE, pp. 1476\u20131480.","DOI":"10.1109\/BigData59044.2023.10386811"},{"key":"190_CR21","doi-asserted-by":"crossref","unstructured":"Kartchner D, Ramalingam S, Al-Hussaini I, Kronick O, and Mitchell C (2023) \"Zero-shot information extraction for clinical meta-analysis using large language models,\": Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.bionlp-1.37"},{"key":"190_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105321","volume":"183","author":"D Hu","year":"2024","unstructured":"Hu D, Liu B, Zhu X, Lu X, Wu N (2024) Zero-shot information extraction from radiological reports using ChatGPT. Int J Med Informatics 183:105321","journal-title":"Int J Med Informatics"},{"key":"190_CR23","unstructured":"Ji B (2023) \"Vicunaner: Zero\/few-shot named entity recognition using vicuna,\" arXiv preprint arXiv:2305.03253"},{"key":"190_CR24","unstructured":"Brown T, et al (2012) \"Language models are few-shot learners advances in neural information processing systems 33,\" 2020."},{"key":"190_CR25","doi-asserted-by":"crossref","unstructured":"Lee Y, Atreya P, Ye X, and Choi E (2023) \"Crafting in-context examples according to LMs\u2019 parametric knowledge,\" arXiv preprint arXiv:2311.09579","DOI":"10.18653\/v1\/2024.findings-naacl.133"},{"key":"190_CR26","doi-asserted-by":"crossref","unstructured":"Rubin O, Herzig J, and Berant J (2021) \"Learning to retrieve prompts for in-context learning,\" arXiv preprint arXiv:2112.08633","DOI":"10.18653\/v1\/2022.naacl-main.191"},{"key":"190_CR27","doi-asserted-by":"crossref","unstructured":"Richter-Pechanski P, et al \"Clinical information extraction for lower-resource languages and domains with few-shot learning using pretrained language models and prompting,\" Natural Language Processing, pp. 1\u201324.","DOI":"10.1017\/nlp.2024.52"},{"issue":"7972","key":"190_CR28","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal K et al (2023) Large language models encode clinical knowledge. Nature 620(7972):172\u2013180","journal-title":"Nature"},{"issue":"3","key":"190_CR29","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1038\/s42256-023-00626-4","volume":"5","author":"N Ding","year":"2023","unstructured":"Ding N et al (2023) Parameter-efficient fine-tuning of large-scale pre-trained language models. Nature Machine Intelligence 5(3):220\u2013235","journal-title":"Nature Machine Intelligence"},{"key":"190_CR30","unstructured":"Xu L, Xie H, Qin S-ZJ, Tao X, and Wang FL (2023) \"Parameter-efficient fine-tuning methods for pretrained language models: a critical review and assessment,\" arXiv preprint arXiv:2312.12148,"},{"key":"190_CR31","doi-asserted-by":"crossref","unstructured":"Gema AP, Minervini P, Daines P, Hope T, and Alex B (2023) \"Parameter-efficient fine-tuning of llama for the clinical domain,\" arXiv preprint arXiv:2307.03042,","DOI":"10.18653\/v1\/2024.clinicalnlp-1.9"},{"key":"190_CR32","unstructured":"Chavan A, Liu Z, Gupta D, Xing E and Shen Z (2023) \"One-for-all: generalized lora for parameter-efficient fine-tuning,\" arXiv preprint arXiv:2306.07967"},{"key":"190_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.103002","volume":"157","author":"N Taylor","year":"2024","unstructured":"Taylor N et al (2024) Efficiency at scale: investigating the performance of diminutive language models in clinical tasks. Artif Intell Med 157:103002","journal-title":"Artif Intell Med"},{"key":"190_CR34","doi-asserted-by":"crossref","unstructured":"Nguyen TT, Wilson C, and Dalins J (2023) \"Fine-tuning llama 2 large language models for detecting online sexual predatory chats and abusive texts,\" arXiv preprint arXiv:2308.14683,","DOI":"10.14428\/esann\/2024.ES2024-222"},{"key":"190_CR35","doi-asserted-by":"crossref","unstructured":"Alkhalaf M, Yu P, Yin M, and Deng C (2024) \"Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records,\" Journal of Biomedical Informatics, p. 104662","DOI":"10.1016\/j.jbi.2024.104662"},{"key":"190_CR36","doi-asserted-by":"crossref","unstructured":"Liu Y (2024) \"The application of RAG technology in traditional Chinese medicine,\" in 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024): Atlantis Press, pp. 402\u2013408","DOI":"10.2991\/978-94-6463-512-6_43"},{"key":"190_CR37","doi-asserted-by":"crossref","unstructured":"R. Singhal, P. Patwa, P. Patwa, A. Chadha, and A. Das, \"Evidence-backed fact checking using RAG and few-shot in-context learning with LLMs,\" arXiv preprint arXiv:2408.12060, 2024.","DOI":"10.18653\/v1\/2024.fever-1.10"},{"key":"190_CR38","unstructured":"C. J. Yang. \"An introduction to RAG and simple\/ complex RAG.\" https:\/\/medium.com\/enterprise-rag\/an-introduction-to-rag-and-simple-complex-rag-9c3aa9bd017b (accessed."},{"key":"190_CR39","unstructured":"Q. Lu, R. Li, A. Wen, J. Wang, L. Wang, and H. Liu, \"Large language models struggle in token-level clinical named entity recognition,\" arXiv preprint arXiv:2407.00731, 2024."},{"issue":"4","key":"190_CR40","doi-asserted-by":"publisher","first-page":"E18","DOI":"10.1111\/ajag.12072","volume":"33","author":"N Wang","year":"2014","unstructured":"Wang N, Bj\u00f6rvell C, Hailey D, Yu P (2014) Development of the Q uality of A ustralian N ursing D ocumentation in A ged C are (QANDAC) instrument to assess paper-based and electronic resident records. Australas J Ageing 33(4):E18\u2013E24","journal-title":"Australas J Ageing"},{"issue":"14","key":"190_CR41","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1111\/j.1365-2702.2008.02670.x","volume":"18","author":"R Daskein","year":"2009","unstructured":"Daskein R, Moyle W, Creedy D (2009) Aged-care nurses\u2019 knowledge of nursing documentation: an Australian perspective. J Clin Nurs 18(14):2087\u20132095","journal-title":"J Clin Nurs"},{"key":"190_CR42","doi-asserted-by":"crossref","unstructured":"Y. Zhu, T. Song, Z. Zhang, M. Yin, and P. Yu, \"A five-step workflow to manually annotate unstructured data into training dataset for natural language processing,\" in MEDINFO 2023\u2014The Future Is Accessible: IOS Press, 2024, pp. 109\u2013113.","DOI":"10.3233\/SHTI230937"},{"issue":"4","key":"190_CR43","doi-asserted-by":"publisher","first-page":"642","DOI":"10.3390\/psychiatryint5040046","volume":"5","author":"Y Zhu","year":"2024","unstructured":"Zhu Y et al (2024) COVID-19 and its influence on prevalence of dementia and agitation in Australian residential aged care: a comparative study. Psychiatry International 5(4):642\u2013659","journal-title":"Psychiatry International"},{"issue":"6","key":"190_CR44","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.3233\/thc-230229","volume":"31","author":"M Alkhalaf","year":"2023","unstructured":"Alkhalaf M et al (2023) \u201cMalnutrition and its contributing factors for older people living in residential aged care facilities: insights from natural language processing of aged care records,\u201d (in eng). Technol Health Care 31(6):2267\u20132278. https:\/\/doi.org\/10.3233\/thc-230229","journal-title":"Technol Health Care"},{"issue":"9","key":"190_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560815","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu P, Yuan W, Fu J, Jiang Z, Hayashi H, Neubig G (2023) Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput Surv 55(9):1\u201335","journal-title":"ACM Comput Surv"},{"key":"190_CR46","unstructured":"Abdallah A, Abdalla M, Elkasaby M, Elbendary Y, and Jatowt A (2023) \"Amurd: annotated multilingual receipts dataset for cross-lingual key information extraction and classification,\" arXiv preprint arXiv:2309.09800,"},{"key":"190_CR47","doi-asserted-by":"crossref","unstructured":"Piao X, Synn D, Park J, and Kim J-K (2023) \"Enabling large batch size training for dnn models beyond the memory limit while maintaining performance,\" IEEE Access, 2023.","DOI":"10.1109\/ACCESS.2023.3312572"},{"key":"190_CR48","unstructured":"Qiao S, Wang H, Liu C, Shen W, and Yuille A (2019) \"Micro-batch training with batch-channel normalization and weight standardization,\" arXiv preprint arXiv:1903.10520"},{"key":"190_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1758-2946-6-10","volume":"6","author":"D Krstajic","year":"2014","unstructured":"Krstajic D, Buturovic LJ, Leahy DE, Thomas S (2014) Cross-validation pitfalls when selecting and assessing regression and classification models. Journal of cheminformatics 6:1\u201315","journal-title":"Journal of cheminformatics"},{"key":"190_CR50","unstructured":"Dandavolu SR \"Fine-tuning and RAG: which one is better?\" https:\/\/www.analyticsvidhya.com\/blog\/2024\/05\/fine-tuning-vs-rag\/ (accessed."},{"key":"190_CR51","unstructured":"Menon K (2024) \"Utilizing open-source AI to navigate and interpret technical documents: leveraging RAG models for enhanced analysis and solutions in product documentation,\""},{"issue":"21","key":"190_CR52","doi-asserted-by":"publisher","first-page":"14603","DOI":"10.1007\/s00521-021-06100-9","volume":"33","author":"JA Kumar","year":"2021","unstructured":"Kumar JA, Abirami S (2021) Ensemble application of bidirectional LSTM and GRU for aspect category detection with imbalanced data. Neural Comput Appl 33(21):14603\u201314621","journal-title":"Neural Comput Appl"},{"key":"190_CR53","doi-asserted-by":"crossref","unstructured":"Pramokchon P, and Piamsa-nga P (2014) \"A feature score for classifying class-imbalanced data,\" in 2014 International Computer Science and Engineering Conference (ICSEC): IEEE, pp. 409\u2013414.","DOI":"10.1109\/ICSEC.2014.6978232"},{"key":"190_CR54","doi-asserted-by":"crossref","unstructured":"Li Y, Li Z, Zhang K, Dan R, Jiang S, and Zhang Y (2023) \"Chatdoctor: a medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge,\" Cureus, 15(6) 2023","DOI":"10.7759\/cureus.40895"},{"issue":"3","key":"190_CR55","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3102\/1076998619832248","volume":"44","author":"J Hao","year":"2019","unstructured":"Hao J, Ho TK (2019) Machine learning made easy: a review of scikit-learn package in python programming language. Journal of Educational and Behavioral Statistics 44(3):348\u2013361","journal-title":"Journal of Educational and Behavioral Statistics"},{"key":"190_CR56","unstructured":"Pedregosa F, et al (2011) \"Scikit-learn: machine learning in Python\". the Journal of machine Learning research, 12: 2825\u20132830"},{"issue":"3","key":"190_CR57","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1093\/biomet\/57.3.579","volume":"57","author":"N Breslow","year":"1970","unstructured":"Breslow N (1970) A generalized Kruskal-Wallis test for comparing K samples subject to unequal patterns of censorship. Biometrika 57(3):579\u2013594","journal-title":"Biometrika"},{"key":"190_CR58","doi-asserted-by":"crossref","unstructured":"Gowda VD, Suneel S, Naidu PR, Ramanan S, and Suneetha S (2024) \"Challenges and limitations of few-shot and zero-shot learning,\" in Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods: IGI Global, 113\u2013137.","DOI":"10.4018\/979-8-3693-1822-5.ch007"},{"key":"190_CR59","unstructured":"Labrak Y, Rouvier M, and Dufour R (2023) \"A zero-shot and few-shot study of instruction-finetuned large language models applied to clinical and biomedical tasks,\" arXiv preprint arXiv:2307.12114"},{"key":"190_CR60","unstructured":"Peng K, et al (2023) \"Exploring few-shot adaptation for activity recognition on diverse domains,\" arXiv preprint arXiv:2305.08420."},{"key":"190_CR61","unstructured":"Han Z, Gao C, Liu J, and Zhang SQ (2024) \"Parameter-efficient fine-tuning for large models: a comprehensive survey,\" arXiv preprint arXiv:2403.14608"},{"key":"190_CR62","doi-asserted-by":"crossref","unstructured":"Abdullahi T, Singh R, and Eickhoff C (2024) \"Retrieval augmented zero-shot text classification,\" in Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval. 195\u2013203.","DOI":"10.1145\/3664190.3672514"},{"key":"190_CR63","doi-asserted-by":"crossref","unstructured":"Bhattarai M, Santos JE, Jones S, Biswas A, Alexandrov B, and O'Malley D (2024) \"Enhancing code translation in language models with few-shot learning via retrieval-augmented generation,\" arXiv preprint arXiv:2407.19619","DOI":"10.1109\/HPEC62836.2024.10938485"},{"key":"190_CR64","unstructured":"Izacard G et al (2022) \"Few-shot learning with retrieval augmented language models,\" arXiv preprint arXiv:2208.032992(3)"},{"key":"190_CR65","doi-asserted-by":"crossref","unstructured":"Soudani H, Kanoulas E, and Hasibi F (2024) \"Fine tuning vs. retrieval augmented generation for less popular knowledge,\" arXiv preprint arXiv:2403.01432","DOI":"10.1145\/3673791.3698415"},{"key":"190_CR66","unstructured":"Salemi A, and Zamani H (2024) \"Comparing retrieval-augmentation and parameter-efficient fine-tuning for privacy-preserving personalization of large language models,\" arXiv preprint arXiv:2409.09510"}],"container-title":["Journal of Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-025-00190-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41666-025-00190-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-025-00190-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T12:36:05Z","timestamp":1745843765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41666-025-00190-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,20]]},"references-count":66,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["190"],"URL":"https:\/\/doi.org\/10.1007\/s41666-025-00190-z","relation":{},"ISSN":["2509-4971","2509-498X"],"issn-type":[{"value":"2509-4971","type":"print"},{"value":"2509-498X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,20]]},"assertion":[{"value":"17 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"All authors have approved the manuscript and agree with its submission to the Journal of Healthcare Informatics Research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}]}}