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Applied Sciences
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Information Retrieval in Health
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Information Retrieval in Health

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 (20 March 2023) | Viewed by 5091

Special Issue Editors

Dr. José Ramón Méndez Reboredo

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Guest Editor
Department of Computer Science, University of Vigo, ESEI-Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
Interests: artificial intelligence; text mining; spam filtering
Special Issues, Collections and Topics in MDPI journals
Dr. David Ruano-Ordás

E-Mail Website
Guest Editor
Department of Electronics and Computer Science, University of Santiago de Compostela, EPSI-Escuela Politécnica Superior de Ingeniería, Campus Terra, 27002 Lugo, Spain
Interests: artificial intelligence; text mining; data mining; drugs discovery; unsupervised clustering schemes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to this Special Issue on Information Retrieval in Health.

This Special Issue is devoted to the application of information retrieval (IR) in the context of health. New contributions to improve the current state of the art in this field are welcomed. Papers can address issues about the application of IR techniques to develop specific solutions for facilitating the work of physicians, to fulfil the information needs of users suffering from a disease, or to provide comprehensive information on illnesses to Internet users. Contributions may concern a wide variety of techniques including but not limited to the following: solutions based on any machine-learning (ML) technique (such as traditional ML models, deep-learning techniques or explainable artificial-intelligence methodologies), word-embedding representations, the use of ontologies or ontology dictionaries, statistical techniques, etc.

The Special Issue is open for the publication of experimental work, properly validated designs for solutions, theoretical studies or state-of-the-art review papers.

Dr. José Ramón Méndez Reboredo
Dr. David Ruano-Ordás
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 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

16 pages, 4822 KiB  
Article
An Anomaly Detection Framework for Twitter Data
by Sandeep Kumar, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdul Khader Jilani Saudagar, Abdullah AlTameem and Mohammed AlKhathami
Appl. Sci. 2022, 12(21), 11059; https://doi.org/10.3390/app122111059 - 01 Nov 2022
Cited by 3 | Viewed by 2406
Abstract
An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can [...] Read more.
An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can be observed in different ways, such as outliers, standard deviation, and noise. Anomaly detection helps us understand the emergence of specific diseases based on health-related tweets. This paper aims to analyze tweets to detect the unusual emergence of healthcare-related tweets, especially pre-COVID-19 and during COVID-19. After pre-processing, this work collected more than 44 thousand tweets and performed topic modeling. Non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) were deployed for topic modeling, and a query set was designed based on resultant topics. This query set was used for anomaly detection using a sentence transformer. K-means was also employed for clustering outlier tweets from the cleaned tweets based on similarity. Finally, an unusual cluster was selected to identify pandemic-like healthcare emergencies. Experimental results show that the proposed framework can detect a sudden rise of unusual tweets unrelated to regular tweets. The new framework was employed in two case studies for anomaly detection and performed with 78.57% and 70.19% accuracy. Full article
(This article belongs to the Special Issue Information Retrieval in Health)
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33 pages, 13728 KiB  
Article
Supporting Clinical COVID-19 Diagnosis with Routine Blood Tests Using Tree-Based Entropy Structured Self-Organizing Maps
by Vagner Sargiani, Alexandra A. De Souza, Danilo Candido De Almeida, Thiago S. Barcelos, Roberto Munoz and Leandro Augusto Da Silva
Appl. Sci. 2022, 12(10), 5137; https://doi.org/10.3390/app12105137 - 19 May 2022
Cited by 4 | Viewed by 1940
Abstract
Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been [...] Read more.
Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. Full article
(This article belongs to the Special Issue Information Retrieval in Health)
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