{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T14:21:24Z","timestamp":1770214884723,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557219","type":"print"},{"value":"9789819557226","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5722-6_11","type":"book-chapter","created":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T08:14:13Z","timestamp":1769933653000},"page":"141-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MIMIC-RxBench: Benchmarking Large Language Models for\u00a0Prescription Error Classification"],"prefix":"10.1007","author":[{"given":"Shuanglin","family":"Zu","sequence":"first","affiliation":[]},{"given":"Yanhong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yibing","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Dapeng","family":"Tao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"ASHP statement on pharmaceutical care. Am. J. Hosp. Pharm. 50(8), 1720\u20131723 (1993). https:\/\/doi.org\/10.1093\/ajhp\/50.8.1720","DOI":"10.1093\/ajhp\/50.8.1720"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Al-Ashwal, F.Y., Zawiah, M., Gharaibeh, L., Abu-Farha, R., Bitar, A.N.: Evaluating the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and bard against conventional drug-drug interactions clinical tools. Drug Healthc. Patient Saf. 137\u2013147 (2023)","DOI":"10.2147\/DHPS.S425858"},{"key":"11_CR3","unstructured":"Ankit Pal, M.S.: OpenbioLLMs: advancing open-source large language models for healthcare and life sciences (2024). https:\/\/huggingface.co\/aaditya\/OpenBioLLM-Llama3-70B"},{"key":"11_CR4","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/s40264-015-0320-x","volume":"38","author":"DM Ashcroft","year":"2015","unstructured":"Ashcroft, D.M., et al.: Prevalence, nature, severity and risk factors for prescribing errors in hospital inpatients: prospective study in 20 UK hospitals. Drug Saf. 38, 833\u2013843 (2015)","journal-title":"Drug Saf."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Balestra, M., Chen, J., Iturrate, E., Aphinyanaphongs, Y., Nov, O.: Predicting inpatient pharmacy order interventions using provider action data. JAMIA Open 4(3), ooab083 (2021)","DOI":"10.1093\/jamiaopen\/ooab083"},{"key":"11_CR6","unstructured":"Chen, J., et al.: HuatuoGPT-o1, towards medical complex reasoning with LLMs. arXiv:2412.18925 (2024)"},{"issue":"11","key":"11_CR7","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1093\/jamia\/ocaa154","volume":"27","author":"J Corny","year":"2020","unstructured":"Corny, J., et al.: A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. J. Am. Med. Inform. Assoc. 27(11), 1688\u20131694 (2020)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"11_CR8","unstructured":"DeepSeek-AI: Deepseek-r1: incentivizing reasoning capability in LLMs via reinforcement learning. arXiv:2501.12948 (2025)"},{"issue":"2","key":"11_CR9","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/JBHI.2018.2828028","volume":"23","author":"HD Dos Santos","year":"2018","unstructured":"Dos Santos, H.D., Ulbrich, A.H.D., Woloszyn, V., Vieira, R.: DDC-outlier: preventing medication errors using unsupervised learning. IEEE J. Biomed. Health Inform. 23(2), 874\u2013881 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"11_CR11","unstructured":"Grattafiori, A., et al.: The Llama 3 herd of models (2024)"},{"issue":"8","key":"11_CR12","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1093\/jamia\/ocab071","volume":"28","author":"SC Hogue","year":"2021","unstructured":"Hogue, S.C., et al.: Pharmacists\u2019 perceptions of a machine learning model for the identification of atypical medication orders. J. Am. Med. Inform. Assoc. 28(8), 1712\u20131718 (2021)","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"14","key":"11_CR13","doi-asserted-by":"publisher","first-page":"6421","DOI":"10.3390\/app11146421","volume":"11","author":"D Jin","year":"2021","unstructured":"Jin, D., Pan, E., Oufattole, N., Weng, W.H., Fang, H., Szolovits, P.: What disease does this patient have? a large-scale open domain question answering dataset from medical exams. Appl. Sci. 11(14), 6421 (2021)","journal-title":"Appl. Sci."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Jin, Q., Dhingra, B., Liu, Z., Cohen, W.W., Lu, X.: PubMedQA: a dataset for biomedical research question answering. arXiv preprint arXiv:1909.06146 (2019)","DOI":"10.18653\/v1\/D19-1259"},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"Johnson, A., Pollard, T., Horng, S., Celi, L.A., Mark, R.: MIMIC-IV-note: deidentified free-text clinical notes (version 2.2). PhysioNet (2023). https:\/\/doi.org\/10.13026\/1n74-ne17","DOI":"10.13026\/1n74-ne17"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Juhi, A., et al.: The capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus 15(3) (2023)","DOI":"10.7759\/cureus.36272"},{"key":"11_CR17","unstructured":"Kaplan, J., et al.: Scaling laws for neural language models. CoRR arXiv:2001.08361 (2020)"},{"issue":"3","key":"11_CR18","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1093\/jamia\/ocv153","volume":"23","author":"TA Kass-Hout","year":"2015","unstructured":"Kass-Hout, T.A., et al.: OpenFDA: an innovative platform providing access to a wealth of FDA\u2019s publicly available data. J. Am. Med. Inform. Assoc. 23(3), 596\u2013600 (2015). https:\/\/doi.org\/10.1093\/jamia\/ocv153","journal-title":"J. Am. Med. Inform. Assoc."},{"issue":"7","key":"11_CR19","doi-asserted-by":"publisher","first-page":"e0254358","DOI":"10.1371\/journal.pone.0254358","volume":"16","author":"CR King","year":"2021","unstructured":"King, C.R., et al.: Predicting self-intercepted medication ordering errors using machine learning. PLoS One 16(7), e0254358 (2021)","journal-title":"PLoS One"},{"key":"11_CR20","unstructured":"Liu, J., et al.: Medchain: Bridging the gap between LLM agents and clinical practice through interactive sequential benchmarking. arXiv preprint arXiv:2412.01605 (2024)"},{"key":"11_CR21","unstructured":"Madaan, A., et al.: Self-refine: iterative refinement with self-feedback. In: Advances in Neural Information Processing Systems, vol. 36, pp. 46534\u201346594 (2023)"},{"issue":"11","key":"11_CR22","doi-asserted-by":"publisher","first-page":"e0260315","DOI":"10.1371\/journal.pone.0260315","volume":"16","author":"K Nagata","year":"2021","unstructured":"Nagata, K., et al.: Detection of overdose and underdose prescriptions\u2013an unsupervised machine learning approach. PLoS One 16(11), e0260315 (2021)","journal-title":"PLoS One"},{"key":"11_CR23","unstructured":"Nori, H., et al.: Can generalist foundation models outcompete special-purpose tuning? case study in medicine. arXiv:2311.16452 (2023). https:\/\/api.semanticscholar.org\/CorpusID:265466787"},{"key":"11_CR24","unstructured":"Ong, J.C.L., et al.: Development and testing of a novel large language model-based clinical decision support systems for medication safety in 12 clinical specialties. arXiv preprint arXiv:2402.01741 (2024)"},{"key":"11_CR25","unstructured":"Organization, W.H.: Global burden of preventable medication-related harm in health care: a systematic review (2024)"},{"issue":"6","key":"11_CR26","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1038\/s41591-024-02933-8","volume":"30","author":"C Pais","year":"2024","unstructured":"Pais, C., Liu, J., Voigt, R., Gupta, V., Wade, E., Bayati, M.: Large language models for preventing medication direction errors in online pharmacies. Nat. Med. 30(6), 1574\u20131582 (2024)","journal-title":"Nat. Med."},{"key":"11_CR27","unstructured":"Pharmaceutical Care Network Europe (PCNE): PCNE classification for drug-related problems V9.1. https:\/\/www.pcne.org\/upload\/files\/555_09_PCNE_classification_V9-1_final.pdf (2020)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Sulaiman, D.M., et al.: Screening the drug-drug interactions between antimicrobials and other prescribed medications using google bard and lexicomp\u00ae $$\\text{online}^{{\\rm TM}}$$ database. Cureus 15(9) (2023)","DOI":"10.7759\/cureus.44961"},{"key":"11_CR29","unstructured":"Team, Q.: Qwq-32b: embracing the power of reinforcement learning (2025). https:\/\/qwenlm.github.io\/blog\/qwq-32b\/"},{"key":"11_CR30","unstructured":"Wang, X., et al.: Self-consistency improves chain of thought reasoning in language models. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=1PL1NIMMrw"},{"key":"11_CR31","doi-asserted-by":"publisher","unstructured":"Wang, Z., Mao, S., Wu, W., Ge, T., Wei, F., Ji, H.: Unleashing the emergent cognitive synergy in large language models: a task-solving agent through multi-persona self-collaboration. In: Duh, K., Gomez, H., Bethard, S. (eds.) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 257\u2013279. Association for Computational Linguistics, Mexico City, Mexico (2024). https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.15, https:\/\/aclanthology.org\/2024.naacl-long.15\/","DOI":"10.18653\/v1\/2024.naacl-long.15"},{"key":"11_CR32","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems, vol. 35, pp. 24824\u201324837 (2022)"},{"issue":"1","key":"11_CR33","doi-asserted-by":"publisher","first-page":"20132","DOI":"10.1038\/s41598-021-99505-4","volume":"11","author":"P Wongyikul","year":"2021","unstructured":"Wongyikul, P., Thongyot, N., Tantrakoolcharoen, P., Seephueng, P., Khumrin, P.: High alert drugs screening using gradient boosting classifier. Sci. Rep. 11(1), 20132 (2021)","journal-title":"Sci. Rep."},{"key":"11_CR34","doi-asserted-by":"publisher","first-page":"1151560","DOI":"10.3389\/fphar.2023.1151560","volume":"14","author":"N Yal\u00e7\u0131n","year":"2023","unstructured":"Yal\u00e7\u0131n, N., et al.: Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit. Front. Pharmacol. 14, 1151560 (2023)","journal-title":"Front. Pharmacol."},{"key":"11_CR35","unstructured":"Yang, A., et al.: Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 (2024)"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5722-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T08:14:15Z","timestamp":1769933655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5722-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557219","9789819557226"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5722-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2025.sau.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}