An ensemble heterogeneous classification methodology for discovering health-related knowledge in social media messages
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TLDR
This paper aims to address the limitations posed by the traditional bag-of-word based methods and propose to use heterogeneous features in combination with ensemble machine learning techniques to discover health-related information, which could prove to be useful to multiple biomedical applications.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2014-06-01 and is currently open access. It has received 105 citations till now. The article focuses on the topics: Document classification & Ensemble learning.read more
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Utilizing social media data for pharmacovigilance
Abeed Sarker,Rachel Ginn,Azadeh Nikfarjam,Karen O'Connor,Karen Smith,Swetha Jayaraman,Tejaswi Upadhaya,Graciela Gonzalez +7 more
TL;DR: A methodical review of the different approaches to ADR detection/extraction from social media, and their applicability to pharmacovigilance suggests that interest in the utilization of the vast amounts of available social media data for ADR monitoring is increasing.
Journal ArticleDOI
A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models
Zeyu Wang,Ravi S. Srinivasan +1 more
TL;DR: An in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple singleAI-based prediction models improves the prediction accuracy manifold.
Journal ArticleDOI
Portable automatic text classification for adverse drug reaction detection via multi-corpus training
Abeed Sarker,Graciela Gonzalez +1 more
TL;DR: The research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks and that integration of information from compatible corpora can significant improve classification performance.
Journal ArticleDOI
Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response
TL;DR: A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications and demonstrates the vast potential of utilizing Twitter forpublic health surveillance purposes.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
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TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.