Search Results for author:
Found 67 papers, 23 papers with code
Generation of Patient After-Visit Summaries to Support Physicians
Crucially, we introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture the important details of the clinical notes or when it contains hallucinated facts that are potentially detrimental to the summary quality.
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models
Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions?
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide.
Automated Identification of Eviction Status from Electronic Health Record Notes
Materials and Methods: We annotated eviction status in 5000 EHR notes from the Veterans Health Administration.
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning
The transfer learning framework helps SOAP classification model's inter-hospital migration with a minimal size of the manually annotated dataset.
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
This task is challenging due to the high-dimensional space of multi-label assignment (155, 000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically.
Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing
Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time.
MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score
We first present a novel and publicly available dataset with expert-annotated medical jargon terms from 18K+ EHR note sentences ($MedJ$).
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3, 000+ tokens.
Ranked #1 on
Medical Code Prediction
on MIMIC-III
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context
In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain.
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale.
Hyperspectral image reconstruction for spectral camera based on ghost imaging via sparsity constraints using V-DUnet
When applying deep learning into GISC spectral camera, there are several challenges need to be solved: 1) how to deal with the large amount of 3D hyperspectral data, 2) how to reduce the influence caused by the uncertainty of the random reference measurements, 3) how to improve the reconstructed image quality as far as possible.
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection
Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention.
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism
Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data.
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.
ScAN: Suicide Attempt and Ideation Events Dataset
In this study, we first built Suicide Attempt and Ideation Events (ScAN) dataset, a subset of the publicly available MIMIC III dataset spanning over 12k+ EHR notes with 19k+ annotated SA and SI events information.
Caption Feature Space Regularization for Audio Captioning
To eliminate this negative effect, in this paper, we propose a two-stage framework for audio captioning: (i) in the first stage, via the contrastive learning, we construct a proxy feature space to reduce the distances between captions correlated to the same audio, and (ii) in the second stage, the proxy feature space is utilized as additional supervision to encourage the model to be optimized in the direction that benefits all the correlated captions.
Attention guided global enhancement and local refinement network for semantic segmentation
First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts.
Category Guided Attention Network for Brain Tumor Segmentation in MRI
In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost.
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images
Different from TransBTS, the proposed TransBTSV2 is not limited to brain tumor segmentation (BTS) but focuses on general medical image segmentation, providing a stronger and more efficient 3D baseline for volumetric segmentation of medical images.
Improving Formality Style Transfer with Context-Aware Rule Injection
Models pre-trained on large-scale regular text corpora often do not work well for user-generated data where the language styles differ significantly from the mainstream text.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
Membership Inference Attack Susceptibility of Clinical Language Models
We show that membership inference attacks on CLMs lead to non-trivial privacy leakages of up to 7%.
A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness
Existing work follows a two-stage pipeline which identifies emotions and causes at the first step and pairs them at the second step.
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides -- A case study of COVID-19 in the UK
A reasonable prediction of infectious diseases transmission process under different disease control strategies is an important reference point for policy makers.
Generating Accurate Electronic Health Assessment from Medical Graph
One of the fundamental goals of artificial intelligence is to build computer-based expert systems.
Dynamic Data Selection for Curriculum Learning via Ability Estimation
Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model.
Conversational Semantic Parsing for Dialog State Tracking
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog.
Ontology-based annotation and analysis of COVID-19 phenotypes
The commonly occurring 17 phenotypes were classified into different groups based on the Human Phenotype Ontology (HPO).
BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab
In addition, the GUI interface enables researchers with limited computer background to compose tools into NLP pipelines and then apply the pipelines on their own datasets in a {``}what you see is what you get{''} (WYSIWYG) way.
Conversational Machine Comprehension: a Literature Review
Conversational Machine Comprehension (CMC), a research track in conversational AI, expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text.
Machine Reading Comprehension
Natural Language Understanding
+1
Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19
We hypothesized that ontology can be used as an integrative platform to classify and analyze HCI and disease outcomes.
Other Quantitative Biology
Neural Data-to-Text Generation with Dynamic Content Planning
We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text.
Calibrating Structured Output Predictors for Natural Language Processing
Additionally, we show that our calibration method can also be used as an uncertainty-aware, entity-specific decoding step to improve the performance of the underlying model at no additional training cost or data requirements.
Continual Domain-Tuning for Pretrained Language Models
Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be tuned for different domains (domain-tuning) by continuing the pre-training phase on a new target domain corpus.
MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation
Manipulating training data leads to robust neural models for MT.
ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network
The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field.
Ranked #9 on
Medical Code Prediction
on MIMIC-III
Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs
abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019.
Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death.
Generating Classical Chinese Poems from Vernacular Chinese
According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models.
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds
We demonstrate a use-case for latent difficulty item parameters, namely training set filtering, and show that using difficulty to sample training data outperforms baseline methods.
HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes
We used this annotated dataset to train and evaluate HYPE, supervised NLP systems for hypoglycemia detection.
Language Identification with Deep Bottleneck Features
In order to improve the SLD accuracy of short utterances a phase vocoder based time-scale modification(TSM) method is used to reduce and increase speech rated of the test utterance.
Histogram Transform-based Speaker Identification
A novel text-independent speaker identification (SI) method is proposed.
Deep Neural Network for Analysis of DNA Methylation Data
Many researches demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer.
Sentence Simplification with Memory-Augmented Neural Networks
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications.
Ranked #1 on
Text Simplification
on PWKP / WikiSmall
Meta Networks
Neural networks have been successfully applied in applications with a large amount of labeled data.
Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients
Objective: The aim of this work was to develop FIT (Finding Important Terms for patients), an unsupervised natural language processing (NLP) system that ranks medical terms in EHR notes based on their importance to patients.
Soft Label Memorization-Generalization for Natural Language Inference
Often when multiple labels are obtained for a training example it is assumed that there is an element of noise that must be accounted for.
Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study
We examine the impact of a test set question's difficulty to determine if there is a relationship between difficulty and performance.
DNN Filter Bank Cepstral Coefficients for Spoofing Detection
In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech.
Ranking medical jargon in electronic health record notes by adapted distant supervision
Methods: We developed an innovative adapted distant supervision (ADS) model based on support vector machines to rank medical jargon from EHRs.
Learning to Rank Scientific Documents from the Crowd
Finding related published articles is an important task in any science, but with the explosion of new work in the biomedical domain it has become especially challenging.
Reasoning with Memory Augmented Neural Networks for Language Comprehension
Hypothesis testing is an important cognitive process that supports human reasoning.
Structured prediction models for RNN based sequence labeling in clinical text
In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks.
Neural Tree Indexers for Text Understanding
NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion.
Ranked #46 on
Natural Language Inference
on SNLI
Neural Semantic Encoders
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders.
Ranked #16 on
Question Answering
on WikiQA
Learning for Biomedical Information Extraction: Methodological Review of Recent Advances
Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research.
Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records
Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs.
Building an Evaluation Scale using Item Response Theory
Evaluation of NLP methods requires testing against a previously vetted gold-standard test set and reporting standard metrics (accuracy/precision/recall/F1).
Heuristic algorithms for finding distribution reducts in probabilistic rough set model
This is partly due to the fact that there are no monotonic fitness functions that are used to design heuristic attribute reduction algorithms in probabilistic rough set model.
