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Diabetic eye disease is a major cause of blindness worldwide.
Medical records that guide and teach
Lawrence L Weed · 1968
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Logistic regression in rare events data
Gary King and Langche Zeng · 2001
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Computerized extraction of information on the quality of diabetes care from free text in electronic patient records of general practitioners
Jaco Voorham and Petra Denig · 2007
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On the stratification of multi-label data
Konstantinos Sechidis, Grigorios Tsoumakas, and Ioannis Vlahavas · 2011
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Semantic characteristics of nlp-extracted concepts in clinical notes vs. biomedical literature
Stephen Wu and Hongfang Liu · 2011
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Towards a semantic lexicon for clinical natural language processing
Hongfang Liu, Stephen T Wu, Dingcheng Li, Siddhartha Jonnalagadda, Sunghwan Sohn, Kavishwar Wagholikar, Peter J Haug, Stanley M Huff, and Christopher G Chute · 2012
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Global prevalence and major risk factors of diabetic retinopathy
Joanne WY Yau, Sophie L Rogers, Ryo Kawasaki, Ecosse L Lamoureux, Jonathan W Kowalski, Toke Bek, Shih-Jen Chen, Jacqueline M Dekker, Astrid Fletcher, Jakob Grauslund, et al · 2012
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Clinical abbreviation disambiguation using neural word embeddings
Yonghui Wu, Jun Xu, Yaoyun Zhang, and Hua Xu · 2015
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Asymmetric multi-task learning based on task relatedness and loss
Giwoong Lee, Eunho Yang, and Sung Hwang · 2016
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Assessing the corpus size vs. similarity trade-off for word embeddings in clinical nlp
Kirk Roberts · 2016
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Diabetic retinopathy: current understanding, mechanisms, and treatment strategies
Elia J Duh, Jennifer K Sun, and Alan W Stitt · 2017
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Natural language processing to ascertain two key variables from operative reports in ophthalmology
Liyan Liu, Neal H Shorstein, Laura B Amsden, and Lisa J Herrinton · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Named entity recognition of electronic medical record in ophthalmology based on crf model
Xingliang Mao, Fangfang Li, Yu Duan, and Hao Wang · 2017
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Enhancing delirium case definitions in electronic health records using clinical free text
Thomas H McCoy Jr, Deanna C Chaukos, Leslie A Snapper, Kamber L Hart, Theodore A Stern, and Roy H Perlis · 2017
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Diabetic retinopathy: a position statement by the american diabetes association
Sharon D Solomon, Emily Chew, Elia J Duh, Lucia Sobrin, Jennifer K Sun, Brian L VanderBeek, Charles C Wykoff, and Thomas W Gardner · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Mittens: an extension of glove for learning domain-specialized representations
Nicholas Dingwall and Christopher Potts · 2018
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Deep learning in ophthalmology: a review
Parampal S Grewal, Faraz Oloumi, Uriel Rubin, and Matthew TS Tennant · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
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Publicly available clinical bert embeddings
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott · 2019
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A survey of word embeddings for clinical text
Faiza Khan Khattak, Serena Jeblee, Chloé Pou-Prom, Mohamed Abdalla, Christopher Meaney, and Frank Rudzicz · 2019
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Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review
Theresa A Koleck, Caitlin Dreisbach, Philip E Bourne, and Suzanne Bakken · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew E Peters, and Noah A Smith · 2019
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Association between diabetic eye disease and other complications of diabetes: implications for care. a systematic review
Ian Pearce, Rafael Simó, Monica Lövestam-Adrian, David T Wong, and Marc Evans · 2019
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Embedded deep learning in ophthalmology: making ophthalmic imaging smarter
Petteri Teikari, Raymond P Najjar, Leopold Schmetterer, and Dan Milea · 2019
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Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting, Louis R Pasquale, Lily Peng, John Peter Campbell, Aaron Y Lee, Rajiv Raman, Gavin Siew Wei Tan, Leopold Schmetterer, Pearse A Keane, and Tien Yin Wong · 2019
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Prevalence of diabetic retinopathy, proliferative diabetic retinopathy and non-proliferative diabetic retinopathy in asian t2dm patients: a systematic review and meta-analysis
Qian-Hui Yang, Yan Zhang, Xiao-Min Zhang, and Xiao-Rong Li · 2019
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Diabetic retinopathy preferred practice pattern®
Christina J Flaxel, Ron A Adelman, Steven T Bailey, Amani Fawzi, Jennifer I Lim, G Atma Vemulakonda, and Gui-shuang Ying · 2020
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Fine-tuning bert for low-resource natural language understanding via active learning
Daniel Grießhaber, Johannes Maucher, and Ngoc Thang Vu · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
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Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clayton Washington, You Chen, Lang Li, Huan Sun, and Yu Su · 2022
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Evaluating pretraining strategies for clinical bert models
Anastasios Lamproudis, Aron Henriksson, and Hercules Dalianis · 2022
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Clinicalt5: A generative language model for clinical text
Qiuhao Lu, Dejing Dou, and Thien Nguyen · 2022
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New meaning for nlp: the trials and tribulations of natural language processing with gpt-3 in ophthalmology
Siddharth Nath, Abdullah Marie, Simon Ellershaw, Edward Korot, and Pearse A Keane · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Biobert: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2020
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Pretrained language models for biomedical and clinical tasks: understanding and extending the state-of-the-art
Patrick Lewis, Myle Ott, Jingfei Du, and Veselin Stoyanov · 2020
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Does bert need domain adaptation for clinical negation detection?
Chen Lin, Steven Bethard, Dmitriy Dligach, Farig Sadeque, Guergana Savova, and Timothy A Miller · 2020
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The utility of general domain transfer learning for medical language tasks
Daniel Ranti, Katie Hanss, Shan Zhao, Varun Arvind, Joseph Titano, Anthony Costa, and Eric Oermann · 2020
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Clinical text data in machine learning: systematic review
Irena Spasic, Goran Nenadic, et al · 2020
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olmpics-on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant · 2020
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Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang R Zhang, and Christopher Ré · 2020
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Large language models encode clinical knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al · 2022
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Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
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Biomedlm: a domain-specific large language model for biomedical text
A Venigalla, J Frankle, and M Carbin · 2022
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Xi Yang, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, et al · 2022
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Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods
Zehao Yu, Xi Yang, Gianna L Sweeting, Yinghan Ma, Skylar E Stolte, Ruogu Fang, and Yonghui Wu · 2022
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Coordinated pausing: An evaluation-based coordination scheme for frontier ai developers
Jide Alaga and Jonas Schuett · 2023
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Open letter: Pause giant ai experiments
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Biomedical language models are robust to sub-optimal tokenization
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Characterization of stigmatizing language in medical records
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Do we still need clinical language models?
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Analyzing leakage of personally identifiable information in language models
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Capabilities of gpt-4 on medical challenge problems
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Fine-tuning large neural language models for biomedical natural language processing
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The shaky foundations of large language models and foundation models for electronic health records
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