Abstract
Patient Innovation is an online open platform, with a community of over 60.000 users and more than 800 innovative solutions developed by patients and informal caregivers from all over the world. These solutions and/or creators were found by manually searching the Web in four different languages, through a combination of appropriate keywords and using experts to curate the results. In this paper we present a classifier architecture composed by a Word2Vec based SVM and a Fuzzy Fingerprint relevance classifier that is able to obtain a F1-score of 0.98 in the process of automatically identifying Patient Innovation solutions from texts obtained from the web.
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Acknowledgements
Work supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) under reference UID/CEC/50021/2013, grant SFRH/BSAB/136312/2018 and project LISBOA-01-0145-FEDER-031474.
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Almeida, J.N., Azevedo, S., Carvalho, J.P. (2020). Towards Automatic Web Identification of Solutions in Patient Innovation. In: Kóczy, L., Medina-Moreno, J., Ramírez-Poussa, E., Šostak, A. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems. Studies in Computational Intelligence, vol 819. Springer, Cham. https://doi.org/10.1007/978-3-030-16024-1_2
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