WonJin Yoon
20 posts
WonJin Yoon
@wonjin_info
Postdoc @ Harvard / Boston Children's Hospital - BioNLP and Clinical NLP
Boston, USA Katılım Ocak 2019
83 Takip Edilen59 Takipçiler
🌿Officially: I’m excited to join the DBMI at CU Anschutz this September as assistant professor! 🏔️
Can’t wait to see the sparkles fly when NLP meets cutting-edge healthcare and medical research at CU Anschutz. Look forward to new collaborations and innovations!
CU Department of Biomedical Informatics (DBMI)@CUBiomedInfo
Yanjun Gao, PhD, (@Serena_pancakes) will join our department as an assistant professor this fall! She will focus on developing foundational natural language processing technologies and conducting research on innovative AI tools in clinical settings. news.cuanschutz.edu/dbmi/yanjun-ga…
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9/9 Most importantly, I would like to thank our esteemed co-authors: @shan23chen, @Serena_pancakes, @dmitriydligach, @dbittermanmd, @Majeans2011, and @tmills(my postdoc PI)! I am very grateful to you all. Thanks to your valuable contributions, this project has reached this point!
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1/9 Excited to introduce the LCD Benchmark! A new benchmark dataset for 30-day out-of-hospital mortality prediction (predicting the future!) using long clinical documents. Designed for testing the capabilities of LMs on lengthy documents! medrxiv.org/content/10.110… #ClinicalNLP
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8/9 Attention Analysis:
Using the hierarchical transformer model and norm-based analysis, we provide insights into which sections of the discharge summaries contribute most to predictions, enhancing model interpretability.
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7/9 Models Evaluated:
* Bag-of-Words
* CNN
* Hierarchical Transformers
* Mixtral
* GPT-4
Our results show that long clinical document processing remains a challenging yet exciting area. Please submit your model's output through CodaBench and compare it with others.
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6/9 Get the Data:
Access the Long Clinical Document (LCD) Benchmark dataset through GitHub (github.com/Machine-Learni…).
Note: You will need to download notes from PhysioNet.org after fully executed MIMIC-IV data use agreement.
#MIMIC4 #MIMICIV
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5/9 Impact:
We expect this benchmark to become a valuable resource for developing and evaluating advanced NLP models tailored for clinical text. It will also facilitate timely end-of-life conversations and improve patient care.
#LCDbenchmark #ClinicalNLP
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3/9 Dataset Highlights:
* Document classification task (Median word count: 1687) - predicting the future
* Sourced from MIMIC-IV discharge notes and statewide death data
* Evaluated with models from BoW and CNN to Hierarchical Transformers and GPT-4
(Figure: # of tokens)
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2/9 Clinical documents are a rich source of information. Yet, most LMs/benchmarks struggle with their length. Our benchmark helps bridge this gap, ensuring models are assessed on real-world, lengthy clinical texts. Leaderboard (#CodaBench) available.
codabench.org/competitions/2…
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We are thrilled to announce a new shared task 😃: Chemotherapy Timelines Extraction from Clinical Text.
Registration (& DUA) is open and training data will be released at the end of this week. sites.google.com/view/chemotime…
The task will be hosted at #NAACL - #ClinicalNLP 2024.
CHIP Informatics@Bos_CHIP
Exciting news We are organizing a shared task. Chemotherapy Treatment Timelines Extraction from the Clinical Narrative (text mining task) Collocated with NAACL 2024, Mexico City, Mexico. Do LLMs solve the task? Check out chemotimelines2024.healthnlp.org
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We were delighted to host Prof. @TMills from @harvardmed to our invited talk session this morning! Thank you very much for the fantastic talk on the ClinicalNLP tasks! All of our lab members (and other participants from the department) enjoyed your talk very much!!
Tim Miller@TMills
It was great to return to visit Prof. Jaewoo Kang's group at Korea University after three years to talk biomedical NLP. Thanks to Prof. Kang and his students (esp. @wonjin_info) for hosting me.
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@BioASQ Thank you so much for giving me a chance to present our work in the best paper track at @clef_initiative. Also many thanks to the organizers of @bioasq for their continuous contribution to the community by holding the challenge.
Anastasios Nentidis@Tasos0000
@wonjin_info presenting the best of the ninth @BioASQ-lab work at #CLEF2022
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Glad to hear this awesome news!
This reminds me of my teenage memories... I used to play with Wolfram Alpha and it was one of my favorite toys.
Back then, I used to have a dream of becoming a mathematician (This dream ended immediately after the first year in Univ 😢😂)
Wolfram@WolframResearch
New in the #WolframNetRepo: Extract text features with a BioBERT Trained on PubMed and PMC Data wolfr.am/OJ0cmwPm Thanks @leejnhk, @wonjin_info and others for creating this model wolfr.am/T2TSCviO #NLP #neuralnetwork #ML #AI
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Here is our way to combat COVID-19. Today we released the Open-domain QA system trained on #COVID19 related articles. CHECK covidsearch.korea.ac.kr We hope our work can help us to overcome this global crisis. #Team_DMIS #NLP #bionlp
Jinhyuk Lee@leejnhk
covidsearch.korea.ac.kr Our first effort to build a real-time QA system on 31K COVID-19 articles with biomedical entities highlighted. It provides answers to all questions in the CORD-19 dataset by @allen_ai. Get your questions answered in just a second! #COVID19
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WonJin Yoon retweetledi
ICML 2020 (icml.cc/Conferences/20…) will be a virtual conference. We've been having discussions with ICLR folks about their plans, and will be learning from their experience (they are up first). We hope to enable as much of the normal ICML experience as possible, virtually.
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Really excited to see BioASQ 8 is on the way. Our team will also participate in this team with a new strategy. Cheers to all the participants, and I look forward to meeting you all at the workshop! #bioasq #bionlp #team_dmis #dmis
BioASQ@BioASQ
The @BioASQ Task 8b is on! Find the 1st batch of Phase A (#biomedical #InformationRetrieval) here participants-area.bioasq.org/Tasks/8b -------- New participants click here to find out how you can join in the #BioASQ #clef2020 Lab! participants-area.bioasq.org #eHealth #BigData #QuestionAnswering
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Our challenge-winning model (#sota for BioASQ task b -exact answers) is available now.
#github github.com/dmis-lab/bioas…
#arxiv arxiv.org/abs/1909.08229
Pic) Me at #BioASQ workshop. Had a wonderful conversations! Thanks for the organizers and participants!!
Thanks for our team too!
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@SantoshStyles Congratulations on your interesting work! Do you have plans to write a paper on that?
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@wonjin_info We did a project with the BioBert weights if you're interested.
github.com/Santosh-Gupta/…
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My second paper on biomedical text processing, BioBERT is now available in arXiv and github!
arxiv.org/abs/1901.08746
Pre-trained model : github.com/naver/biobert-…
Fine-tuning codes : github.com/dmis-lab/biobe…
#arxiv #BioBERT #BERT #nlp #bionlp #ner #re #qa
Jinhyuk Lee@leejnhk
Our paper BioBERT (arxiv.org/abs/1901.08746) is now available in arxiv with pre-training weights and fine tuning codes! Pre-training on biomedical corpus helps a lot on multiple biomedical text mining tasks including NER, RE, QA, etc. :)
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