research
∙
02/16/2023
Do We Still Need Clinical Language Models?
Although recent advances in scaling large language models (LLMs) have re...
share
research
∙
02/01/2023
Using Machine Learning to Develop Smart Reflex Testing Protocols
Objective: Reflex testing protocols allow clinical laboratories to perfo...
research
∙
06/06/2022
Learning to Ask Like a Physician
Existing question answering (QA) datasets derived from electronic health...
research
∙
12/05/2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
The increasing availability of large collections of electronic health re...
research
∙
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...
research
∙
01/31/2021
Adversarial Contrastive Pre-training for Protein Sequences
Recent developments in Natural Language Processing (NLP) demonstrate tha...
research
∙
09/28/2020
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Open domain question answering (OpenQA) tasks have been recently attract...
research
∙
08/22/2020
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
We propose and demonstrate a novel machine learning algorithm that asses...
research
∙
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...
research
∙
07/01/2020
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
Knowledge Graphs (KG), composed of entities and relations, provide a str...
research
∙
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...
research
∙
06/23/2020
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
Offline Reinforcement Learning (RL) is a promising approach for learning...
research
∙
05/13/2020
Entity-Enriched Neural Models for Clinical Question Answering
We explore state-of-the-art neural models for question answering on elec...
research
∙
04/04/2020
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Current summarization systems only produce plain, factual headlines, but...
research
∙
01/22/2020
Unsupervised Domain Adaptation for Neural Machine Translation with Iterative Back Translation
State-of-the-art neural machine translation (NMT) systems are data-hungr...
research
∙
09/19/2019
Representation Learning for Electronic Health Records
Information in electronic health records (EHR), such as clinical narrati...
research
∙
07/27/2019
Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment
Machine learning algorithms are often vulnerable to adversarial examples...
research
∙
06/19/2019
Reflex: Flexible Framework for Relation Extraction in Multiple Domains
Systematic comparison of methods for relation extraction (RE) is difficu...
research
∙
04/04/2019
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs ...
research
∙
02/04/2019
Unsupervised Clinical Language Translation
As patients' access to their doctors' clinical notes becomes common, tra...
research
∙
12/03/2018
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability
Determining whether hypotensive patients in intensive care units (ICUs) ...
research
∙
11/21/2018
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Joint embeddings between medical imaging modalities and associated radio...
research
∙
11/15/2018
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
This paper presents a Lisp architecture for a portable NLP system, terme...
research
∙
10/30/2018
Advancing PICO Element Detection in Medical Text via Deep Neural Networks
In evidence-based medicine (EBM), structured medical questions are alway...
research
∙
08/19/2018
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
Prevalent models based on artificial neural network (ANN) for sentence c...
research
∙
08/11/2018
Racial Disparities and Mistrust in End-of-Life Care
There are established racial disparities in healthcare, including during...
research
∙
06/30/2018
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a ma...
research
∙
06/25/2018
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment
Mapping and translating professional but arcane clinical jargons to cons...
research
∙
03/07/2018
Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes
Clinical notes often describe the most important aspects of a patient's ...
research
∙
03/06/2018
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
Clinical notes often describe important aspects of a patient's stay and ...
research
∙
11/27/2017
Deep Reinforcement Learning for Sepsis Treatment
Sepsis is a leading cause of mortality in intensive care units and costs...
research
∙
05/17/2017
Transfer Learning for Named-Entity Recognition with Neural Networks
Recent approaches based on artificial neural networks (ANNs) have shown ...
research
∙
05/16/2017
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Named-entity recognition (NER) aims at identifying entities of interest ...
research
∙
04/05/2017
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Over 50 million scholarly articles have been published: they constitute ...
research
∙
12/15/2016
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
Existing models based on artificial neural networks (ANNs) for sentence ...
research
∙
10/30/2016
Feature-Augmented Neural Networks for Patient Note De-identification
Patient notes contain a wealth of information of potentially great inter...
research
∙
06/10/2016
De-identification of Patient Notes with Recurrent Neural Networks
Objective: Patient notes in electronic health records (EHRs) may contain...
research
∙
02/27/2013