Special Issue "Invited Papers from the pHealth 2019 Conference, Genoa, Italy, 10-12 June 2019"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Prof. Dr. Bernd Blobel
E-Mail Website
Guest Editor
eHealth Competence Center, University of Regensburg Medical Center, Germany
Prof. Dr. Mauro Giacomini
E-Mail Website
Guest Editor
University of Genoa, Dept. of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS)

Special Issue Information

Dear Colleagues,

You have been invited to submit a journal version of your paper presented at the pHealth 2019 Conference in Genoa, 10-12 June 2019, selected by the International Jury for publication in the pHealth 2019 Special Issue of IJERPH (Impact Factor 2.468). This Special Issue will be guest edited by Prof. Dr. Bernd Blobel and Prof. Dr. Mauro Giacomini. Please submit your manuscripts before 30 September 2019. See the Special Issue website for further details and submission instructions. Participants of this conference will receive a 20% discount on the Article Processing Charges.

Papers submitted to this Special Issue of IJERPH will undergo the standard peer-review procedure. Published papers will be indexed by the SCIE (Web of Science) and PubMed.

Prof. Dr. Bernd Blobel
Prof. Dr. Mauro Giacomini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle
Comparison of Word Embeddings for Extraction from Medical Records
Int. J. Environ. Res. Public Health 2019, 16(22), 4360; https://doi.org/10.3390/ijerph16224360 - 08 Nov 2019
Abstract
This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from [...] Read more.
This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763). Full article
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