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Found 32 papers, 1 papers with code
Emory at WNUT-2020 Task 2: Combining Pretrained Deep Learning Models and Feature Enrichment for Informative Tweet Identification
This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”.
Benchmarking of Transformer-Based Pre-Trained Models on Social Media Text Classification Datasets
We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy.
Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications
This paper describes our approach for six classification tasks (Tasks 1a, 3a, 3b, 4 and 5) and one span detection task (Task 1b) from the Social Media Mining for Health (SMM4H) 2021 shared tasks.
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021
The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health.
Overview of the Fifth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2020
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
An Ensemble Model for Automatic Grading of Evidence
This paper describes our approach for the automatic grading of evidence task from the Australasian Language Technology Association (ALTA) Shared Task 2021.
Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022
For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content.
Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data
We developed three approaches for leveraging LLMs for text classification: employing LLMs as zero-shot classifiers, us-ing LLMs as annotators to annotate training data for supervised classifiers, and utilizing LLMs with few-shot examples for augmentation of manually annotated data.
Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing
This analysis highlighted not only the utility of NLP techniques in unstructured social media data to identify self-reported breast cancer posts, medication usage patterns, and treatment side effects but also the richness of social data on such clinical questions.
Learning from Two Decades of Blood Pressure Data: Demography-Specific Patterns Across 75 Million Patient Encounters
Hypertension remains a global health concern with a rising prevalence, necessitating effective monitoring and understanding of blood pressure (BP) dynamics.
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
Based on the automatically-extracted information from these articles, we conducted an analysis of the variations of BP values across biological sex.
Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media
In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem.
Social media mining for toxicovigilance of prescription medications: End-to-end pipeline, challenges and future work
We developed a sophisticated end-to-end pipeline for mining information about nonmedical prescription medication use from social media, namely Twitter and Reddit.
Few-shot learning for medical text: A systematic review
We aimed to conduct a systematic review to explore the state of FSL methods for medical NLP.
Towards Automatic Bot Detection in Twitter for Health-related Tasks
Our approach obtains F_1 scores of 0. 7 for the "bot" class, representing improvements of 0. 339.
Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019
We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users.
Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets
A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets.
Automatically Detecting Self-Reported Birth Defect Outcomes on Twitter for Large-scale Epidemiological Research
The primary objective of this study was to take the first step towards scaling the use of social media for observing pregnancies with birth defect outcomes, namely, developing methods for automatically detecting tweets by users reporting their birth defect outcomes.
Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018
The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets.
An unsupervised and customizable misspelling generator for mining noisy health-related text sources
Our proposed spelling variant generator has several advantages over the current state-of-the-art and other types of variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable.
HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors
We present a simple supervised text classification system that combines sparse and dense vector representations of words, and generalized representations of words via clusters.
Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System
Social media sites (e. g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data.
Automated text summarisation and evidence-based medicine: A survey of two domains
We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.
Social media mining for identification and exploration of health-related information from pregnant women
We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information.
Data, tools and resources for mining social media drug chatter
In this paper, we discuss the preparation of these guidelines, outline the data sets prepared, and present an overview of our state-of-the-art systems for data collection, supervised classification, and information extraction.
Mining the Web for Pharmacovigilance: the Case Study of Duloxetine and Venlafaxine
Adverse reactions caused by drugs following their release into the market are among the leading causes of death in many countries.
Automated Extraction of Number of Subjects in Randomised Controlled Trials
We present a simple approach for automatically extracting the number of subjects involved in randomised controlled trials (RCT).
