Matthew B. A. McDermott | DeepAI

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∙ 06/20/2023

Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events

Generative, pre-trained transformers (GPTs, a.k.a. "Foundation Models") ...

0 Matthew B. A. McDermott, et al. ∙

∙ 11/30/2021

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021

A collection of the accepted abstracts for the Machine Learning for Heal...

1 Fabian Falck, et al. ∙

∙ 03/18/2021

Rethinking Relational Encoding in Language Model: Pre-Training for General Sequences

Language model pre-training (LMPT) has achieved remarkable results in na...

0 Matthew B. A. McDermott, et al. ∙

∙ 01/31/2021

Adversarial Contrastive Pre-training for Protein Sequences

Recent developments in Natural Language Processing (NLP) demonstrate tha...

0 Matthew B. A. McDermott, et al. ∙

∙ 11/19/2020

ML4H Abstract Track 2020

A collection of the accepted abstracts for the Machine Learning for Heal...

0 Emily Alsentzer, et al. ∙

∙ 07/20/2020

A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data

Multi-task learning (MTL) is a machine learning technique aiming to impr...

10 Matthew B. A. McDermott, et al. ∙

∙ 06/26/2020

CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output

It is often infeasible or impossible to obtain ground truth labels for m...

0 Matthew B. A. McDermott, et al. ∙

∙ 02/05/2020

ML4H Abstract Track 2019

A collection of the accepted abstracts for the Machine Learning for Heal...

0 Matthew B. A. McDermott, et al. ∙

∙ 11/22/2019

Approaching Small Molecule Prioritization as a Cross-Modal Information Retrieval Task through Coordinated Representation Learning

Modeling the relationship between chemical structure and molecular activ...

0 Samuel G. Finlayson, et al. ∙

∙ 08/02/2019

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 ...

0 Bret Nestor, et al. ∙

∙ 07/19/2019

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 s...

0 Shirly Wang, et al. ∙

∙ 07/02/2019

Reproducibility in Machine Learning for Health

Machine learning algorithms designed to characterize, monitor, and inter...

16 Matthew B. A. McDermott, et al. ∙

∙ 06/19/2019

Reflex: Flexible Framework for Relation Extraction in Multiple Domains

Systematic comparison of methods for relation extraction (RE) is difficu...

0 Geeticka Chauhan, et al. ∙

∙ 04/06/2019

Publicly Available Clinical BERT Embeddings

Contextual word embedding models such as ELMo (Peters et al., 2018) and ...

0 Emily Alsentzer, et al. ∙

∙ 11/30/2018

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

Machine learning for healthcare often trains models on de-identified dat...

0 Bret Nestor, et al. ∙

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