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Rare diseases (RDs) are collectively common and affect 300 million people worldwide.
Quality of life in rare genetic conditions: a systematic review of the literature
Julie S Cohen and Barbara B Biesecker · 2010
Earlier work this paper cites.
Representation of rare diseases in health information systems: the orphanet approach to serve a wide range of end users
Ana Rath, Annie Olry, Ferdinand Dhombres, Maja Miličić Brandt, Bruno Urbero, and Segolene Ayme · 2012
Earlier work this paper cites.
Brat: a web-based tool for nlp-assisted text annotation
Pontus Stenetorp, Sampo Pyysalo, Goran Topić, Tomoko Ohta, Sophia Ananiadou, and Jun’ichi Tsujii · 2012
Earlier work this paper cites.
Automated extraction of clinical traits of multiple sclerosis in electronic medical records
Mary F Davis, Subramaniam Sriram, William S Bush, Joshua C Denny, and Jonathan L Haines · 2013
Earlier work this paper cites.
Quantifying a rare disease in administrative data: the example of calciphylaxis
Sagar U Nigwekar, Craig A Solid, Elizabeth Ankers, Rajeev Malhotra, William Eggert, Alexander Turchin, Ravi I Thadhani, and Charles A Herzog · 2014
Earlier work this paper cites.
The national institutes of health undiagnosed diseases program
Cynthia J Tifft and David R Adams · 2014
Earlier work this paper cites.
“is it going to hurt?”: the impact of the diagnostic odyssey on children and their families
Nikkola Carmichael, Judith Tsipis, Gail Windmueller, Leslie Mandel, and Elicia Estrella · 2015
Earlier work this paper cites.
Biomedical named entity recognition based on extended recurrent neural networks
Lishuang Li, Liuke Jin, Zhenchao Jiang, Dingxin Song, and Degen Huang · 2015
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 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
Earlier work this paper cites.
Deep neural models for extracting entities and relationships in the new rdd corpus relating disabilities and rare diseases
Hermenegildo Fabregat, Lourdes Araujo, and Juan Martinez-Romo · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Clinical information extraction applications: a literature review
Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, et al · 2018
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Publicly available clinical bert embeddings
Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott · 2019
Cited alongside, same era.
The undiagnosed diseases program: Approach to diagnosis
Ellen F Macnamara, Precilla D’Souza, Cynthia J Tifft, et al · 2019
Cited alongside, same era.
Computable phenotype implementation for a national, multicenter pragmatic clinical trial: lessons learned from adaptable
Faraz S Ahmad, Iben M Ricket, Bradley G Hammill, Lisa Eskenazi, Holly R Robertson, Lesley H Curtis, Cecilia D Dobi, Saket Girotra, Kevin Haynes, Jorge R Kizer, et al · 2020
Cited alongside, same era.
Barriers to rare disease diagnosis, care and treatment in the us: a 30-year comparative analysis, 2020
NORD Rare Insights · 2020
Cited alongside, same era.
Estimating cumulative point prevalence of rare diseases: analysis of the orphanet database
Stéphanie Nguengang Wakap, Deborah M Lambert, Annie Olry, Charlotte Rodwell, Charlotte Gueydan, Valérie Lanneau, Daniel Murphy, Yann Le Cam, and Ana Rath · 2020
Large language models are few-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag · 2022
Later among the works it cites.
Rare disease emerging as a global public health priority
Claudia Ching Yan Chung, Hong Kong Genome Project, Annie Tsz Wai Chu, and Brian Hon Yin Chung · 2022
Later among the works it cites.
Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su · 2022
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The raredis corpus: a corpus annotated with rare diseases, their signs and symptoms
Claudia Martínez-deMiguel, Isabel Segura-Bedmar, Esteban Chacón-Solano, and Sara Guerrero-Aspizua · 2022
Later among the works it cites.
Introducing chatgpt
OpenAI · 2022
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Cited alongside, same era.
Named entity recognition using conditional random fields
Nita Patil, Ajay Patil, and BV Pawar · 2020
Cited alongside, same era.
Using computable phenotypes in point-of-care clinical trial recruitment
Martin Chapman, Jesús Domínguez, Elliot Fairweather, Brendan Delaney, and Vasa Curcin · 2021
Cited alongside, same era.
Lightner: a lightweight tuning paradigm for low-resource ner via pluggable prompting
Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, and Ningyu Zhang · 2021
Cited alongside, same era.
The therapeutic odyssey: Positioning genomic sequencing in the search for a child’s best possible life
Janet Elizabeth Childerhose, Carla Rich, Kelly M East, Whitley V Kelley, Shirley Simmons, Candice R Finnila, Kevin Bowling, Michelle Amaral, Susan M Hiatt, Michelle Thompson, et al · 2021
Cited alongside, same era.
Template-based named entity recognition using bart
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang · 2021
Cited alongside, same era.
Improving early diagnosis of rare diseases using natural language processing in unstructured medical records: an illustration from dravet syndrome
Tommaso Lo Barco, Mathieu Kuchenbuch, Nicolas Garcelon, Antoine Neuraz, and Rima Nabbout · 2021
Cited alongside, same era.
Template-free prompt tuning for few-shot ner
Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, and Xuanjing Huang · 2021
Cited alongside, same era.
Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts
Isabel Segura-Bedmar, David Camino-Perdones, and Sara Guerrero-Aspizua · 2022
Later among the works it cites.
Clinical prompt learning with frozen language models
Niall Taylor, Yi Zhang, Dan Joyce, Alejo Nevado-Holgado, and Andrey Kormilitzin · 2022
Later among the works it cites.
The national economic burden of rare disease in the united states in 2019
Grace Yang, Inna Cintina, Anne Pariser, Elisabeth Oehrlein, Jamie Sullivan, and Annie Kennedy · 2022
Later among the works it cites.
Qingyu Chen, Jingcheng Du, Yan Hu, Vipina Kuttichi Keloth, Xueqing Peng, Kalpana Raja, Rui Zhang, Zhiyong Lu, and Hua Xu · 2023
Closest in time.
Zero-shot clinical entity recognition using chatgpt
Yan Hu, Iqra Ameer, Xu Zuo, Xueqing Peng, Yujia Zhou, Zehan Li, Yiming Li, Jianfu Li, Xiaoqian Jiang, and Hua Xu · 2023
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The ai revolution in medicine: Gpt-4 and beyond, 2023
Peter Lee, Carey Goldberg, and Isaac Kohane · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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Chatgpt as a medical doctor? a diagnostic accuracy study on common and rare diseases
Lars Mehnen, Stefanie Gruarin, Mina Vasileva, and Bernhard Knapp · 2023
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