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Machine learning in medicine: a practical introduction to natural language processing ...
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Abstract:
Abstract Background Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of written text into interpretable datasets that can be analysed by statistical and machine learning (ML) models. The purpose of this paper is to provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software. Methods We performed three NLP experiments using publicly-available data obtained from medicine review websites. First, we conducted lexicon-based sentiment analysis on open-text patient reviews of four drugs: Levothyroxine, Viagra, Oseltamivir and Apixaban. Next, we used unsupervised ML (latent Dirichlet allocation, LDA) to identify similar drugs in the dataset, based solely on their reviews. Finally, we developed three supervised ML algorithms to predict whether a drug review was associated ...
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Keyword:
Artificial Intelligence and Image Processing; FOS Computer and information sciences
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URL: https://springernature.figshare.com/collections/Machine_learning_in_medicine_a_practical_introduction_to_natural_language_processing/5537977/1 https://dx.doi.org/10.6084/m9.figshare.c.5537977.v1
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Machine learning in medicine: a practical introduction to natural language processing ...
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Machine learning in medicine: a practical introduction to natural language processing
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In: BMC Med Res Methodol (2021)
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