Search Results for author:
Found 98 papers, 38 papers with code
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.
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).
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.
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.
Neural Semantic Encoders
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders.
Ranked #18 on
Question Answering
on WikiQA
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
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.
Reasoning with Memory Augmented Neural Networks for Language Comprehension
Hypothesis testing is an important cognitive process that supports human reasoning.
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.
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.
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.
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.
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.
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.
Meta Networks
Neural networks have been successfully applied in applications with a large amount of labeled data.
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
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.
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.
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.
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.
Generating Classical Chinese Poems from Vernacular Chinese
According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models.
Cultural Vocal Bursts Intensity Prediction
Reinforcement Learning (RL)
+2
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.
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.
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
MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation
Manipulating training data leads to robust neural models for MT.
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.
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.
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.
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
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
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.
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).
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.
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.
Generating Accurate Electronic Health Assessment from Medical Graph
One of the fundamental goals of artificial intelligence is to build computer-based expert systems.
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.
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 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.
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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 ($MedJ$).
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.
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.
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.
Automated Identification of Eviction Status from Electronic Health Record Notes
Materials and Methods: We first defined eviction status (eviction presence and eviction period) and then annotated eviction status in 5000 EHR notes from the Veterans Health Administration (VHA).
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.
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?
Boosting Few-Shot Text Classification via Distribution Estimation
Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.
Few-Shot Image Classification
Few-Shot Text Classification
+1
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information
Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided.
Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond
Is the output softmax layer, which is adopted by most language models (LMs), always the best way to compute the next word probability?
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem.
Referring to Screen Texts with Voice Assistants
We collect a dataset and propose a lightweight general-purpose model for this novel experience.
UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets.
ODD: A Benchmark Dataset for the Natural Language Processing based Opioid Related Aberrant Behavior Detection
Experimental results show that the prompt-tuning models outperformed the fine-tuning models in most categories and the gains were especially higher among uncommon categories (Suggested Aberrant Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and Medication Change).
Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences from Longitudinal Electronic Health Records of US Military Veterans
We used a panel of AD-related keywords and their occurrences over time in a patient's longitudinal EHRs as predictors for AD prediction with four machine learning models.
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously.
Intelligent Assistant Language Understanding On Device
It has recently become feasible to run personal digital assistants on phones and other personal devices.
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
Patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs).
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues.
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn.
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors
Decision-based methods have shown to be effective in black-box adversarial attacks, as they can obtain satisfactory performance and only require to access the final model prediction.
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing
We also examined whether categories(e. g., QA, IE, and generation) of instructions impact model performance.
EHRTutor: Enhancing Patient Understanding of Discharge Instructions
This paper presents EHRTutor, an innovative multi-component framework leveraging the Large Language Model (LLM) for patient education through conversational question-answering.
Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
In this work, we propose a new pipeline using ChatGPT instead of human experts to generate high-quality feedback data for improving factual consistency in the clinical note summarization task.
MARRS: Multimodal Reference Resolution System
Successfully handling context is essential for any dialog understanding task.
SELF-EXPLAIN: Teaching Large Language Models to Reason Complex Questions by Themselves
Large language models (LLMs) can generate intermediate reasoning steps.
Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.
Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data
We train a system to detect AD-related signs and symptoms from EHRs, using three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method; and (3) a bronze dataset created by the label-to-data method.
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR).
EHR Interaction Between Patients and AI: NoteAid EHR Interaction
Through a comprehensive evaluation of the entire dataset using LLM assessment and a rigorous manual evaluation of 64 instances, we showcase the potential of LLMs in patient education.
Can Large Language Models Understand Context?
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text
Black-box hard-label adversarial attack on text is a practical and challenging task, as the text data space is inherently discrete and non-differentiable, and only the predicted label is accessible.
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data.
LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data
Prior research on Twitter (now X) data has provided positive evidence of its utility in developing supplementary health surveillance systems.
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
Large Language Models (LLMs) such as GPT & Llama have demonstrated significant achievements in summarization tasks but struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences.
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
Unlike previous methods in RAG where the retrieval model was trained separately from the LLM, we introduce JMLR (for Jointly trains LLM and information Retrieval (IR)) during the fine-tuning phase.
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
The advancement of natural language processing (NLP) systems in healthcare hinges on language model ability to interpret the intricate information contained within clinical notes.
ReALM: Reference Resolution As Language Modeling
Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds.
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.
