1 code implementation • NAACL (TextGraphs) 2021 • Yanjun Gao, Ting-Hao Huang, Rebecca J. Passonneau
We design a neural model to learn a semantic representation for clauses from graph convolution over latent representations of the subject and verb phrase.
no code implementations • COLING 2022 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. M. Churpek, Majid Afshar
In this work, we propose a new NLP task that aims to generate a list of problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization.
no code implementations • 7 Feb 2025 • Shihan Fu, Bingsheng Yao, Smit Desai, Yuqi Hu, Yuling Sun, Samantha Stonbraker, Yanjun Gao, Elizabeth M. Goldberg, Dakuo Wang
Our project aims to explore the information needs of caregivers of ICU older adult patients, from which we can propose design opportunities to guide future AI systems.
no code implementations • 30 Jan 2025 • Maya Kruse, Shiyue Hu, Nicholas Derby, YiFu Wu, Samantha Stonbraker, Bingsheng Yao, Dakuo Wang, Elizabeth Goldberg, Yanjun Gao
Recent advancements in large language models (LLMs) have shown potential for transforming data processing in healthcare, particularly in understanding complex clinical narratives.
no code implementations • 15 Jan 2025 • Emma Croxford, Yanjun Gao, Nicholas Pellegrino, Karen K. Wong, Graham Wills, Elliot First, Miranda Schnier, Kyle Burton, Cris G. Ebby, Jillian Gorskic, Matthew Kalscheur, Samy Khalil, Marie Pisani, Tyler Rubeor, Peter Stetson, Frank Liao, Cherodeep Goswami, Brian Patterson, Majid Afshar
Validation included Pearson correlation for substantive validity, factor analysis and Cronbach's alpha for structural validity, inter-rater reliability (ICC and Krippendorff's alpha) for generalizability, a semi-Delphi process for content validity, and comparisons of high-versus low-quality summaries for discriminant validity.
no code implementations • 7 Nov 2024 • Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy A Miller, Danielle Bitterman, Guanhua Chen, Anoop Mayampurath, Matthew Churpek, Majid Afshar
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited.
no code implementations • 26 Sep 2024 • Emma Croxford, Yanjun Gao, Nicholas Pellegrino, Karen K. Wong, Graham Wills, Elliot First, Frank J. Liao, Cherodeep Goswami, Brian Patterson, Majid Afshar
Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text.
no code implementations • 23 Sep 2024 • Skatje Myers, Timothy A. Miller, Yanjun Gao, Matthew M. Churpek, Anoop Mayampurath, Dmitriy Dligach, Majid Afshar
We also examine the choice of embedding pooling strategy for each model, independently on the query and the text to retrieve.
no code implementations • 15 Aug 2024 • Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy A Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering.
1 code implementation • 6 May 2024 • Ruizhe Li, Yanjun Gao
By updating these vectors within MLP and recalibrating attention patterns to neutralise the preference for the first choice 'A', we effectively mitigate the anchored bias.
1 code implementation • 28 Mar 2024 • Shan Chen, Jack Gallifant, Marco Guevara, Yanjun Gao, Majid Afshar, Timothy Miller, Dmitriy Dligach, Danielle S. Bitterman
Generative models have been showing potential for producing data in mass.
1 code implementation • 5 Mar 2024 • Xin Chen, Hanxian Huang, Yanjun Gao, Yi Wang, Jishen Zhao, Ke Ding
Knowledge distillation, the technique of transferring knowledge from large, complex models to smaller ones, marks a pivotal step towards efficient AI deployment.
no code implementations • 5 Feb 2024 • Li Yu, Yanjun Gao, Farhad Pakdaman, Moncef Gabbouj
In response to these challenges, we propose a panoramic image inpainting framework that consists of a Face Generator, a Cube Generator, a side branch, and two discriminators.
no code implementations • 28 Aug 2023 • Yanjun Gao, Ruizhe Li, John Caskey, Dmitriy Dligach, Timothy Miller, Matthew M. Churpek, Majid Afshar
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
no code implementations • 8 Jun 2023 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M. Churpek, Majid Afshar
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023.
no code implementations • 7 Jun 2023 • Brihat Sharma, Yanjun Gao, Timothy Miller, Matthew M. Churpek, Majid Afshar, Dmitriy Dligach
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors.
no code implementations • 14 Mar 2023 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Matthew M Churpek, Ozlem Uzuner, Majid Afshar
The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes.
no code implementations • 29 Sep 2022 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, John Caskey, Brihat Sharma, Matthew M Churpek, Majid Afshar
The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated.
no code implementations • 17 Aug 2022 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. Churpek, Majid Afshar
In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization.
no code implementations • LREC 2022 • Yanjun Gao, Dmitriy Dligach, Timothy Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, Majid Afshar
This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization.
no code implementations • 7 Dec 2021 • Yanjun Gao, Dmitriy Dligach, Leslie Christensen, Samuel Tesch, Ryan Laffin, Dongfang Xu, Timothy Miller, Ozlem Uzuner, Matthew M Churpek, Majid Afshar
Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community.
no code implementations • 10 Sep 2021 • Yanjun Gao, Lulu Liu, Jason Wang, Xin Chen, Huayan Wang, Rui Zhang
Given a query and an untrimmed video, the temporal grounding model predicts the target interval, and the predicted video clip is fed into a video translation task by generating a simplified version of the input query.
2 code implementations • ACL 2021 • Yanjun Gao, Ting-Hao, Huang, Rebecca J. Passonneau
On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.
1 code implementation • CONLL 2019 • Yanjun Gao, Chen Sun, Rebecca J. Passonneau
Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts.
1 code implementation • WS 2019 • Yanjun Gao, Alex Driban, Brennan Xavier McManus, Elena Musi, Patricia Davies, Smar Muresan, a, Rebecca J. Passonneau
We present a unique dataset of student source-based argument essays to facilitate research on the relations between content, argumentation skills, and assessment.
no code implementations • WS 2018 • Yanjun Gao, Patricia M. Davies, Rebecca J. Passonneau
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