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Found 50 papers, 26 papers with code
emrKBQA: A Clinical Knowledge-Base Question Answering Dataset
We perform experiments to validate the quality of the dataset and set benchmarks for question to logical form learning that helps answer questions on this dataset.
Towards Generalizable Methods for Automating Risk Score Calculation
Clinical risk scores enable clinicians to tabulate a set of patient data into simple scores to stratify patients into risk categories.
Advancing Seq2seq with Joint Paraphrase Learning
We address the problem of model generalization for sequence to sequence (seq2seq) architectures.
Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings
Previous research on expert advice-taking shows that humans exhibit two contradictory behaviors: on the one hand, people tend to overvalue their own opinions undervaluing the expert opinion, and on the other, people often defer to other people's advice even if the advice itself is rather obviously wrong.
Do We Still Need Clinical Language Models?
To investigate this question, we conduct an extensive empirical analysis of 12 language models, ranging from 220M to 175B parameters, measuring their performance on 3 different clinical tasks that test their ability to parse and reason over electronic health records.
Using Machine Learning to Develop Smart Reflex Testing Protocols
Here, using the analyte ferritin as an example, we propose an alternative machine learning-based approach to "smart" reflex testing with a wider scope and greater impact than traditional rule-based approaches.
Learning to Ask Like a Physician
The questions are generated by medical experts from 100+ MIMIC-III discharge summaries.
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
In this review, we focus on the interpretability of the DL models in healthcare.
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
Structure Inducing Pre-Training
Based on this review, we introduce a descriptive framework for pre-training that allows for a granular, comprehensive understanding of how relational structure can be induced.
Adversarial Contrastive Pre-training for Protein Sequences
Recent developments in Natural Language Processing (NLP) demonstrate that large-scale, self-supervised pre-training can be extremely beneficial for downstream tasks.
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 attracting more and more attention from the natural language processing (NLP) community.
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
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 improve model performance by leveraging information across many tasks.
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
CheXpert is very useful, but is relatively computationally slow, especially when integrated with end-to-end neural pipelines, is non-differentiable so can't be used in any applications that require gradients to flow through the labeler, and does not yield probabilistic outputs, which limits our ability to improve the quality of the silver labeler through techniques such as active learning.
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
However, the adoption of such policies in practice is often challenging, as they are hard to interpret within the application context, and lack measures of uncertainty for the learned policy value and its decisions.
Entity-Enriched Neural Models for Clinical Question Answering
We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time.
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure.
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data.
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text
Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks.
Representation Learning for Electronic Health Records
Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning.
A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains
Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus.
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models.
REflex: Flexible Framework for Relation Extraction in Multiple Domains
Systematic comparison of methods for relation extraction (RE) is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques.
Clinically Accurate Chest X-Ray Report Generation
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.
Unsupervised Clinical Language Translation
As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-clinician communication.
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) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict.
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Joint embeddings between medical imaging modalities and associated radiology reports have the potential to offer significant benefits to the clinical community, ranging from cross-domain retrieval to conditional generation of reports to the broader goals of multimodal representation learning.
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations.
Advancing PICO Element Detection in Biomedical Text via Deep Neural Networks
One is the PubMed-PICO dataset, where our best results outperform the previous best by 5. 5%, 7. 9%, and 5. 8% for P, I, and O elements in terms of F1 score, respectively.
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear.
Ranked #1 on
Sentence Classification
on PubMed 20k RCT
Racial Disparities and Mistrust in End-of-Life Care
There are established racial disparities in healthcare, including during end-of-life care, when poor communication and trust can lead to suboptimal outcomes for patients and their families.
Applications
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks
Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases.
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a machine learning-derived trust score.
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment
In this work, we utilized the embeddings alignment method for the word mapping between unparalleled clinical professional and consumer language embeddings.
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.
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients
This work aims to learn personalized optimal glycemic trajectories for severely ill septic patients by learning data-driven policies to identify optimal targeted blood glucose levels as a reference for clinicians.
Deep Reinforcement Learning for Sepsis Treatment
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually.
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learning.
Clinical Intervention Prediction and Understanding using Deep Networks
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs).
Transfer Learning for Named-Entity Recognition with Neural Networks
In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Named-entity recognition (NER) aims at identifying entities of interest in a text.
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Over 50 million scholarly articles have been published: they constitute a unique repository of knowledge.
The Use of Autoencoders for Discovering Patient Phenotypes
We use autoencoders to create low-dimensional embeddings of underlying patient phenotypes that we hypothesize are a governing factor in determining how different patients will react to different interventions.
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually.
Feature-Augmented Neural Networks for Patient Note De-identification
In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system.
De-identification of Patient Notes with Recurrent Neural Networks
It yields an F1-score of 97. 85 on the i2b2 2014 dataset, with a recall 97. 38 and a precision of 97. 32, and an F1-score of 99. 23 on the MIMIC de-identification dataset, with a recall 99. 25 and a precision of 99. 06.
MIMIC-III, a freely accessible critical care database
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.
