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This paper describes PULSAR, our system submission at the ImageClef 2023 MediQA-Sum task on summarising patient-doctor dialogues into clinical records.
Language models are few-shot learners,
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C.-Y. Lin, · 2004
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Use of electronic clinical documentation: time spent and team interactions,
G. Hripcsak, D. K. Vawdrey, M. R. Fred, S. B. Bostwick, · 2011
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2010 i2b2/va challenge on concepts, assertions, and relations in clinical text,
Ö. Uzuner, B. R. South, S. Shen, S. L. DuVall, · 2011
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Emergent abilities of large language models,
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, et al., · 2011
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Mimic-iii, a freely accessible critical care database,
A. E. Johnson, T. J. Pollard, L. Shen, L.-w. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. Anthony Celi, R. G. Mark, · 2016
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Quickumls: a fast, unsupervised approach for medical concept extraction,
L. Soldaini, N. Goharian, · 2016
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Automatically generating psychiatric case notes from digital transcripts of doctor-patient conversations using text mining,
N. Kazi, I. Kahanda, · 2019
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Deep learning in generating radiology reports: A survey,
M. M. A. Monshi, J. Poon, V. Chung, · 2020
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Generating medical reports from patient-doctor conversations using sequence-to-sequence models,
S. Enarvi, M. Amoia, M. D.-A. Teba, B. Delaney, F. Diehl, S. Hahn, K. Harris, L. McGrath, Y. Pan, J. Pinto, et al., · 2020
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Dr. summarize: Global summarization of medical dialogue by exploiting local structures,
A. Joshi, N. Katariya, X. Amatriain, A. Kannan, · 2020
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Language models are few-shot learners,
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei, · 2020
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Bleurt: Learning robust metrics for text generation,
T. Sellam, D. Das, A. Parikh, · 2020
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Bertscore: Evaluating text generation with bert,
T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, Y. Artzi, · 2020
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Spanbert: Improving pre-training by representing and predicting spans,
M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, O. Levy, · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer,
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, P. J. Liu, · 2020
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Towards automating medical scribing: Clinic visit dialogue2note sentence alignment and snippet summarization,
W.-w. Yim, M. Yetisgen-Yildiz, · 2021
Cited alongside, same era.
Generating datasets with pretrained language models,
T. Schick, H. Schütze, · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization,
Scaling instruction-finetuned language models,
H. W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, et al., · 2022
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Llm. int8 (): 8-bit matrix multiplication for transformers at scale,
T. Dettmers, M. Lewis, Y. Belkada, L. Zettlemoyer, · 2022
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Lora: Low-rank adaptation of large language models,
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen, et al., · 2022
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Discharge summary hospital course summarisation of in patient electronic health record text with clinical concept guided deep pre-trained transformer models,
T. Searle, Z. Ibrahim, J. Teo, R. J. Dobson, · 2023
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Bionlp workshop 2023 shared task 1a: Problem list summarization,
Y. Gao, T. Miller, M. Afshar, D. Dligach, · 2023
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V. Sanh, A. Webson, C. Raffel, S. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, A. Raja, M. Dey, et al., · 2022
Cited alongside, same era.
Large language models are few-shot clinical information extractors,
M. Agrawal, S. Hegselmann, H. Lang, Y. Kim, D. Sontag, · 2022
Cited alongside, same era.
Large language models encode clinical knowledge,
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl, et al., · 2022
Cited alongside, same era.
Ai in health and medicine,
P. Rajpurkar, E. Chen, O. Banerjee, E. J. Topol, · 2022
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A survey on dialogue summarization: Recent advances and new frontiers,
X. Feng, X. Feng, B. Qin, · 2022
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model,
T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, M. Gallé, et al., · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback,
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., · 2022
Cited alongside, same era.
Closest in time.
Overview of the mediqa-sum task at imageclef 2023: Summarization and classification of doctor-patient conversations,
W. Yim, A. Ben Abacha, N. Snider, G. Adams, M. Yetisgen, · 2023
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Mimic-iv-icd: A new benchmark for extreme multilabel classification,
T.-T. Nguyen, V. Schlegel, A. Kashyap, S. Winkler, S.-S. Huang, J.-J. Liu, C.-J. Lin, · 2023
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An empirical study of clinical note generation from doctor-patient encounters,
A. B. Abacha, W. Yim, Y. Fan, T. Lin, · 2023
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Pulsar: Pre-training with extracted healthcare terms for summarising patients’ problems and data augmentation with black-box large language models,
H. Li, Y. Wu, V. Schlegel, R. Batista-Navarro, T.-T. Nguyen, A. Ramesh Kashyap, X. Zeng, D. Beck, S. Winkler, G. Nenadic, · 2023
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H. Li, V. Schlegel, R. Batista-Navarro, G. Nenadic, · 2023
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Do we still need clinical language models?,
E. Lehman, E. Hernandez, D. Mahajan, J. Wulff, M. J. Smith, Z. Ziegler, D. Nadler, P. Szolovits, A. Johnson, E. Alsentzer, · 2023
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Large ai models in health informatics: Applications, challenges, and the future,
J. Qiu, L. Li, J. Sun, J. Peng, P. Shi, R. Zhang, Y. Dong, K. Lam, F. P.-W. Lo, B. Xiao, et al., · 2023
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