ArticleBMC medical informatics and decision making2019
Extracting health-related causality from twitter messages using natural language processing.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- One size fits all: Enhanced zero-shot text classification for patient listening on social media.Frontiers in artificial intelligence · 2024Article
- Building causal models for finding actual causes of unmanned aerial vehicle failures.Frontiers in robotics and AI · 2024Article
- Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.Sleep & breathing = Schlaf & Atmung · 2023Review
- Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing.JMIR formative research · 2022Article
- Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach.JMIR medical informatics · 2022Article
- Exploring Public Perceptions of Dental Care Affordability in the United States: Mixed Method Analysis via Twitter.JMIR formative research · 2022Article
- Identifying Cases of Shoulder Injury Related to Vaccine Administration (SIRVA) in the United States: Development and Validation of a Natural Language Processing Method.JMIR public health and surveillance · 2022Article
- Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review.BMC medical research methodology · 2022Article
- Natural language processing applied to mental illness detection: a narrative review.NPJ digital medicine · 2022Review
- Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things.Computational intelligence and neuroscience · 2022Article
- Consistency of causal claims in observational studies: a review of papers published in a general medical journal.BMJ open · 2021Review
- Building a semantically annotated corpus for chronic disease complications using two document types.PloS one · 2021Article
- Special issue of BMC medical informatics and decision making on health natural language processing.BMC medical informatics and decision making · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTwitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques.
methodsLexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: "stress", "insomnia", and "headache." A large dataset consisting of 24 million tweets are used.
resultsThe results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations.
conclusionsManual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.
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