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Found 23 papers, 5 papers with code
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization
One of the cardinal tasks in achieving robust consumer health question answering systems is the question summarization and multi-document answer summarization.
Towards Summarizing Healthcare Questions in Low-Resource Setting
The current advancement in abstractive document summarization depends to a large extent on a considerable amount of human-annotated datasets.
Detecting Optimism in Tweets using Knowledge Distillation and Linguistic Analysis of Optimism
Later, we devised a multi-task knowledge distillation framework to simultaneously learn the target task of optimism detection with the help of the auxiliary task of sentiment analysis and hate speech detection.
No perspective, no perception!! Perspective-aware Healthcare Answer Summarization
While several efforts have been made to summarize the community answers, most of them are limited to the open domain and overlook the different perspectives offered by these answers.
Aspect-oriented Consumer Health Answer Summarization
We build an automated multi-faceted answer summarization pipeline with this dataset based on task-specific fine-tuning of several state-of-the-art models.
Abstractive Text Summarization
Community Question Answering
+1
Towards Enhancing Health Coaching Dialogue in Low-Resource Settings
In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy.
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
The quest for seeking health information has swamped the web with consumers' health-related questions.
Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction
The recent advancement of pre-trained Transformer models has propelled the development of effective text mining models across various biomedical tasks.
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
Question-aware Transformer Models for Consumer Health Question Summarization
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework
Existing studies on using social media for deriving mental health status of users focus on the depression detection task.
Medical Knowledge-enriched Textual Entailment Framework
One of the cardinal tasks in achieving robust medical question answering systems is textual entailment.
"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction
In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression.
Assessing the Severity of Health States based on Social Media Posts
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework
Towards this, we model the relation extraction problem in multi-task learning (MTL) framework and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text.
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining
The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance.
Relation extraction between the clinical entities based on the shortest dependency path based LSTM
In this paper, we present an efficient relation extraction system based on the shortest dependency path (SDP) generated from the dependency parsed tree of the sentence.
Leveraging Medical Sentiment to Understand Patients Health on Social Media
A large percentage of this population is actively engaged in health social networks to share health-related information.
Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts
In this paper, we present a novel method based on deep bidirectional long short-term memory (B-LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text.
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment
In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog.
Entity Extraction in Biomedical Corpora: An Approach to Evaluate Word Embedding Features with PSO based Feature Selection
The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG, and AiMed.
Deep Learning Architecture for Patient Data De-identification in Clinical Records
However, medical records enclose patient Private Health Information (PHI) which can reveal the identities of the patients.
