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In the era of Large Language Models (LLMs), given their remarkable text understanding and generation abilities, there is an unprecedented opportunity to develop new, LLM-based methods for trustworthy medical knowledge synthesis, extraction and summarization.
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László Babai · 1985
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Actor-critic algorithms
Vijay Konda and John Tsitsiklis · 1999
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The new sentinel network—improving the evidence of medical-product safety
Richard Platt, Marcus Wilson, K Arnold Chan, Joshua S Benner, Janet Marchibroda, and Mark McClellan · 2009
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Carl Hewitt · 2010
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Advancing the science for active surveillance: rationale and design for the observational medical outcomes partnership
Paul E Stang, Patrick B Ryan, Judith A Racoosin, J Marc Overhage, Abraham G Hartzema, Christian Reich, Emily Welebob, Thomas Scarnecchia, and Janet Woodcock · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Defining a reference set to support methodological research in drug safety
Patrick B Ryan, Martijn J Schuemie, Emily Welebob, Jon Duke, Sarah Valentine, and Abraham G Hartzema · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Learning from observational databases: Lessons from omop and ohdsi
David Madigan and Patrick Ryan · 2015
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Design and analysis choices for safety surveillance evaluations need to be tuned to the specifics of the hypothesized drug–outcome association
Susan Gruber, Aloka Chakravarty, Susan R Heckbert, Mark Levenson, David Martin, Jennifer C Nelson, Bruce M Psaty, Simone Pinheiro, Christian G Reich, Sengwee Toh, et al · 2016
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Evidence of misclassification of drug–event associations classified as gold standard ‘negative controls’ by the observational medical outcomes partnership (omop)
Manfred Hauben, Jeffrey K Aronson, and Robin E Ferner · 2016
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Markov logic networks for adverse drug event extraction from text
Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vitor Santos Costa, David Page, and Michael Caldwell · 2017
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Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications
Justin Mower, Devika Subramanian, and Trevor Cohen · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jeff Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels
Mert Tiftikci, Arzucan Özgür, Yongqun He, and Junguk Hur · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Ade eval: an evaluation of text processing systems for adverse event extraction from drug labels for pharmacovigilance
Samuel Bayer, Cheryl Clark, Oanh Dang, John Aberdeen, Sonja Brajovic, Kimberley Swank, Lynette Hirschman, and Robert Ball · 2021
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Bioreader: a retrieval-enhanced text-to-text transformer for biomedical literature
Giacomo Frisoni, Miki Mizutani, Gianluca Moro, and Lorenzo Valgimigli · 2022
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Predicting adverse drug reactions from social media posts: Data balance, feature selection and deep learning
Jhih-Yuan Huang, Wei-Po Lee, and King-Der Lee · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Artificial intelligence–enabled software prototype to inform opioid pharmacovigilance from electronic health records: Development and usability study
Alfred Sorbello, Syed Arefinul Haque, Rashedul Hasan, Richard Jermyn, Ahmad Hussein, Alex Vega, Krzysztof Zembrzuski, Anna Ripple, and Mitra Ahadpour · 2023
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Evaluating large language models on medical evidence summarization
Liyan Tang, Zhaoyi Sun, Betina Idnay, Jordan G Nestor, Ali Soroush, Pierre A Elias, Ziyang Xu, Ying Ding, Greg Durrett, Justin F Rousseau, et al · 2023
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Medagents: Large language models as collaborators for zero-shot medical reasoning
Xiangru Tang, Anni Zou, Zhuosheng Zhang, Yilun Zhao, Xingyao Zhang, Arman Cohan, and Mark Gerstein · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Langroid: Multi-agent framework for llm applications
Prasad Chalasani, Nils Palumbo, Mohannad Alhanahnah, and Somesh Jha · 2023
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Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
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Retrieve, summarize, and verify: how will chatgpt affect information seeking from the medical literature?
Qiao Jin, Robert Leaman, and Zhiyong Lu · 2023
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API-bank: A comprehensive benchmark for tool-augmented LLMs
Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li · 2023
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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark · 2023
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Capabilities of gpt-4 on medical challenge problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz · 2023
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Future of chatgpt in pharmacovigilance
H. Wang, Y.J. Ding, and Y. Luo · 2023
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Augmenting black-box llms with medical textbooks for clinical question answering
Yubo Wang, Xueguang Ma, and Wenhu Chen · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang · 2023
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The rise and potential of large language model based agents: A survey
Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al · 2023
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MetaGPT: Meta programming for multi-agent collaborative framework
Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber · 2024
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Rxnorm for drug name normalization: a case study of prescription opioids in the fda adverse events reporting system
Huyen Le, Ru Chen, Stephen Harris, Hong Fang, Beverly Lyn-Cook, Huixiao Hong, Weigong Ge, Paul Rogers, Weida Tong, and Wen Zou · 2024
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Leveraging ChatGPT in pharmacovigilance event extraction: An empirical study
Zhaoyue Sun, Gabriele Pergola, Byron Wallace, and Yulan He · 2024
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Daedra: A language model for predicting outcomes in passive pharmacovigilance reporting
Chris von Csefalvay · 2024
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Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs
Miao Xiong, Zhiyuan Hu, Xinyang Lu, YIFEI LI, Jie Fu, Junxian He, and Bryan Hooi · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2024
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Almanac — retrieval-augmented language models for clinical medicine
Cyril Zakka, Rohan Shad, Akash Chaurasia, Alex R. Dalal, Jennifer L. Kim, Michael Moor, Robyn Fong, Curran Phillips, Kevin Alexander, Euan Ashley, Jack Boyd, Kathleen Boyd, Karen Hirsch, Curt Langlotz, Rita Lee, Joanna Melia, Joanna Nelson, Karim Sallam, Stacey Tullis, Melissa Ann Vogelsong, John Patrick Cunningham, and William Hiesinger · 2024
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