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Applied Sciences
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Integration and Mining of Data from Mobile Devices
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Integration and Mining of Data from Mobile Devices

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 6816

Special Issue Editor

Prof. Dr. Ernestina Menasalvas

E-Mail Website
Guest Editor
Centro de Tecnología Biomédica, Campus de Montegancedo, Universidad Politécnica de Madrid, 28233 Pozuelo de Alarcón, Spain
Interests: big data; predictive analytics; data mining; data stream mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The amount of mobile devices people is using in their daily activities is generating a rich amount of data regarding processes, people, and behavior that can be analyzed in search of patterns. These mobiles devices, besides recording information regarding specific uses of the device (health, telemonitoring, communication, social, and weather), also gather geographical information most of the time. The integration of geographical information, together with social networking information, health parameters, traffic, and weather conditions, to name but a few, could be used as a basis to generate patterns and knowledge that can be used to build intelligent applications. However, the whole process, from gathering the information from these mobile devices to extracting knowledge, still requires research and improvements in computation and storage, at least. The later also creates challenges concerning the best way to compute models in mobile devices.

This Special Issue aims at collecting high-quality papers on recent advances and reviews that address the challenges of harvesting, integration, preparation, modelling, the deployment of models generated with data from mobile devices, and the computation of models in mobile devices. Special attention will be paid to real applications that arise from these models and that integrate different nature data such as social, geographical, weather, and health data.

Prof. Dr. Ernestina Menasalvas
Guest Editor

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 submissions that pass pre-check are 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. Applied Sciences 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 2400 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 (2 papers)

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Research

21 pages, 673 KiB  
Article
Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes
by Oswaldo Solarte Pabón, Maria Torrente, Mariano Provencio, Alejandro Rodríguez-Gonzalez and Ernestina Menasalvas
Appl. Sci. 2021, 11(2), 865; https://doi.org/10.3390/app11020865 - 19 Jan 2021
Cited by 12 | Viewed by 3347
Abstract
Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, [...] Read more.
Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, we concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date. In particular, we apply the proposed approach to extract the lung cancer diagnosis and its diagnosis date from clinical narratives written in Spanish. Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date. Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes. Full article
(This article belongs to the Special Issue Integration and Mining of Data from Mobile Devices)
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13 pages, 2948 KiB  
Article
Identifying Polarity in Tweets from an Imbalanced Dataset about Diseases and Vaccines Using a Meta-Model Based on Machine Learning Techniques
by Alejandro Rodríguez-González, Juan Manuel Tuñas, Lucia Prieto Santamaría, Diego Fernández Peces-Barba, Ernestina Menasalvas Ruiz, Almudena Jaramillo, Manuel Cotarelo, Antonio J. Conejo Fernández, Amalia Arce and Angel Gil
Appl. Sci. 2020, 10(24), 9019; https://doi.org/10.3390/app10249019 - 17 Dec 2020
Cited by 7 | Viewed by 2589
Abstract
Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several [...] Read more.
Sentiment analysis is one of the hottest topics in the area of natural language. It has attracted a huge interest from both the scientific and industrial perspective. Identifying the sentiment expressed in a piece of textual information is a challenging task that several commercial tools have tried to address. In our aim of capturing the sentiment expressed in a set of tweets retrieved for a study about vaccines and diseases during the period 2015–2018, we found that some of the main commercial tools did not allow an accurate identification of the sentiment expressed in a tweet. For this reason, we aimed to create a meta-model which used the results of the commercial tools to improve the results of the tools individually. As part of this research, we had to deal with the problem of unbalanced data. This paper presents the main results in creating a metal-model from three commercial tools to the correct identification of sentiment in tweets by using different machine-learning techniques and methods and dealing with the unbalanced data problem. Full article
(This article belongs to the Special Issue Integration and Mining of Data from Mobile Devices)
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