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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

ACM transactions on computing for healthcare, 2021-10, Vol.3 (1), p.1-23, Article 2

2021

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Natural Language Processing for EHR-Based Computational Phenotyping

IEEE/ACM transactions on computational biology and bioinformatics, 2019-01, Vol.16 (1), p.139-153

2019

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The role of machine learning in clinical research: transforming the future of evidence generation

Trials, 2021-08, Vol.22 (1), p.537-537, Article 537

2021

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Practical guidance on artificial intelligence for health-care data

The Lancet. Digital health, 2019-08, Vol.1 (4), p.e157-e159

2019

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Making big data useful for health care: a summary of the inaugural mit critical data conference

JMIR medical informatics, 2014-08, Vol.2 (2), p.e22-e22

2014

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Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

PLOS digital health, 2024-04, Vol.3 (4), p.e0000474-e0000474

2024

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Correction to: The role of machine learning in clinical research: transforming the future of evidence generation

Trials, 2021-09, Vol.22 (1), p.593-593, Article 593

2021

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Fine-tuning large neural language models for biomedical natural language processing

Patterns (New York, N.Y.), 2023-04, Vol.4 (4), p.100729-100729, Article 100729

2023

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

Computer Vision - ECCV 2022, 2022, Vol.13696, p.1-21

2022

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A whole-slide foundation model for digital pathology from real-world data

Nature (London), 2024-06, Vol.630 (8015), p.181-188

2024

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A "datathon" model to support cross-disciplinary collaboration

Science translational medicine, 2016-04, Vol.8 (333), p.333ps8

2016

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Trends and Focus of Machine Learning Applications for Health Research

JAMA network open, 2019-10, Vol.2 (10), p.e1914051-e1914051

2019

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Leveraging Text Representations for Clinical Predictive Tasks

Naumann, Tristan

2018

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Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

PLOS digital health, 2024-04, Vol.3 (4), p.e0000474

2024

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Datathons and Software to Promote Reproducible Research

Journal of medical Internet research, 2016-08, Vol.18 (8), p.e230-e230

2016

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Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision

Patterns (New York, N.Y.), 2023-04, Vol.4 (4), p.100726-100726, Article 100726

2023

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Compositional Zero-Shot Domain Transfer with Text-to-Text Models

Transactions of the Association for Computational Linguistics, 2023-09, Vol.11, p.1097-1113

2023

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What's in a Note? Unpacking Predictive Value in Clinical Note Representations

AMIA Summits on Translational Science proceedings, 2018, Vol.2017, p.26-34

2018

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A Review of Challenges and Opportunities in Machine Learning for Health

AMIA Summits on Translational Science proceedings, 2020, Vol.2020, p.191-200

2020

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Unfolding physiological state: mortality modelling in intensive care units

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, p.75-84

2014

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Precision Health in the Age of Large Language Models

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, p.5825-5826

2023

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Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, p.1497-1505

2017

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Metadata correction: making big data useful for health care: a summary of the inaugural mit critical data conference

JMIR medical informatics, 2015-01, Vol.3 (1), p.e6-e6

2015

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Scaling the PhysioNet WFDB Toolbox for MATLAB and Octave

Computing in Cardiology 2014, 2014, p.161-164

2014

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Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, p.3717-3725

2021

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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Natural Language Processing for EHR-Based Computational Phenotyping

The role of machine learning in clinical research: transforming the future of evidence generation

Practical guidance on artificial intelligence for health-care data

Making big data useful for health care: a summary of the inaugural mit critical data conference

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Correction to: The role of machine learning in clinical research: transforming the future of evidence generation

Fine-tuning large neural language models for biomedical natural language processing

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

A whole-slide foundation model for digital pathology from real-world data

A "datathon" model to support cross-disciplinary collaboration

Trends and Focus of Machine Learning Applications for Health Research

Leveraging Text Representations for Clinical Predictive Tasks

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Datathons and Software to Promote Reproducible Research

Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision

Compositional Zero-Shot Domain Transfer with Text-to-Text Models

What's in a Note? Unpacking Predictive Value in Clinical Note Representations

A Review of Challenges and Opportunities in Machine Learning for Health

Unfolding physiological state: mortality modelling in intensive care units

Precision Health in the Age of Large Language Models

Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Metadata correction: making big data useful for health care: a summary of the inaugural mit critical data conference

Scaling the PhysioNet WFDB Toolbox for MATLAB and Octave

Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature

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