Search Results for author: Tristan Naumann

Found 34 papers, 12 papers with code

Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events

no code implementations12 Jul 2023 Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, Hoifung Poon

We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access.

Self-Supervised Learning

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

no code implementations NeurIPS 2023 Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao

In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.

Instruction Following Language Modelling +2

Self-Verification Improves Few-Shot Clinical Information Extraction

1 code implementation30 May 2023 Zelalem Gero, Chandan Singh, Hao Cheng, Tristan Naumann, Michel Galley, Jianfeng Gao, Hoifung Poon

Extracting patient information from unstructured text is a critical task in health decision-support and clinical research.

In-Context Learning

Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making

1 code implementation27 May 2023 Aliyah R. Hsu, Yeshwanth Cherapanamjeri, Briton Park, Tristan Naumann, Anobel Y. Odisho, Bin Yu

These findings showcase the utility of SUFO in enhancing trust and safety when using transformers in medicine, and we believe SUFO can aid practitioners in evaluating fine-tuned language models for other applications in medicine and in more critical domains.

Decision Making

What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization

1 code implementation12 May 2023 Griffin Adams, Bichlien H Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad

Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE).

Continual Contrastive Finetuning Improves Low-Resource Relation Extraction

no code implementations21 Dec 2022 Wenxuan Zhou, Sheng Zhang, Tristan Naumann, Muhao Chen, Hoifung Poon

In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.

Contrastive Learning Relation +3

Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing

1 code implementation21 Apr 2022 Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay

We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing.

Contrastive Learning Language Modelling +4

Knowledge-Rich Self-Supervision for Biomedical Entity Linking

no code implementations15 Dec 2021 Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.

Contrastive Learning Entity Linking

Modular Self-Supervision for Document-Level Relation Extraction

no code implementations EMNLP 2021 Sheng Zhang, Cliff Wong, Naoto Usuyama, Sarthak Jain, Tristan Naumann, Hoifung Poon

Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.

Document-level Relation Extraction Reading Comprehension +1

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

1 code implementation31 Jul 2020 Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.

Continual Pretraining +11

Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation

no code implementations4 Dec 2019 Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi

We learn mappings from other languages to English and detect aphasia from linguistic characteristics of speech, and show that OT domain adaptation improves aphasia detection over unilingual baselines for French (6% increased F1) and Mandarin (5% increased F1).

Domain Adaptation

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

1 code implementation2 Aug 2019 Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.

De-identification Length-of-Stay prediction +1

MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III

2 code implementations19 Jul 2019 Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi

Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced.

BIG-bench Machine Learning Length-of-Stay prediction +3

Publicly Available Clinical BERT Embeddings

2 code implementations WS 2019 Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months.


Generalizability of predictive models for intensive care unit patients

1 code implementation6 Dec 2018 Alistair E. W. Johnson, Tom J. Pollard, Tristan Naumann

A large volume of research has considered the creation of predictive models for clinical data; however, much existing literature reports results using only a single source of data.

Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

no code implementations30 Nov 2018 Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi

Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time.

BIG-bench Machine Learning Mortality Prediction

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

no code implementations17 Nov 2018 Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers, Adrian Dalca, Sam Finlayson, Madalina Fiterau, Jason Alan Fries, Marzyeh Ghassemi, Mike Hughes, Bruno Jedynak, Jasvinder S. Kandola, Matthew McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi

This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.

BIG-bench Machine Learning

Natural Language Processing for EHR-Based Computational Phenotyping

no code implementations13 Jun 2018 Zexian Zeng, Yu Deng, Xiaoyu Li, Tristan Naumann, Yuan Luo

This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.

Computational Phenotyping

Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes

no code implementations7 Mar 2018 Willie Boag, Tristan Naumann, Peter Szolovits

Clinical notes often describe the most important aspects of a patient's physiology and are therefore critical to medical research.


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