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Found 21 papers, 5 papers with code
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction
In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
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.
A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022
A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022.
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Results: We conduct an extensive study on 135, 107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states.
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Overall, domainspecific vocabulary and pretraining facilitate more robust models for fine-tuning.
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
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.
Modular Self-Supervision for Document-Level Relation Extraction
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.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
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.
Ranked #2 on
Participant Intervention Comparison Outcome Extraction
on EBM-NLP
(using extra training data)
ML4H Abstract Track 2019
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019.
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
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).
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
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.
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
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.
Ranked #3 on
Length-of-Stay prediction
on MIMIC-III
Publicly Available Clinical BERT Embeddings
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
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
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.
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
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.
Natural Language Processing for EHR-Based Computational Phenotyping
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.
A Review of Challenges and Opportunities in Machine Learning for Health
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions.
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes
Clinical notes often describe the most important aspects of a patient's physiology and are therefore critical to medical research.
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research.
