Special Issue "Feature Papers: Health Informatics"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Health Informatics".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Kamran Sedig
Website
Guest Editor
Department of Computer Science and The Faculty of Information & Media Studies, Western University, London, Ontario, N6A 5B7, Canada
Interests: computer science; information science; design; human-computer interaction; visualization; cognition, learning, and motivation sciences
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Health informatics (also called health care informatics, healthcare informatics, medical informatics, nursing informatics, clinical informatics, or biomedical informatics) is information engineering applied to the field of health care—essentially the management and use of patient health care information. It is a multidisciplinary field that uses health information technology (HIT) to improve health care via any combination of higher quality, higher efficiency (spurring lower cost and thus greater availability), and new opportunities. The disciplines involved include information science, computer science, social science, behavioral science, management science, and others.

Our Special Issue is keen to receive and publish high-quality submissions on any subject relevant to health informatics. For well-prepared papers and those approved for further publication, authors might be eligible for discounts for publication.

Dr. Kamran Sedig
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 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. Informatics is an international peer-reviewed open access quarterly 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 1000 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.

Keywords

  • health informatics
  • health care informatics
  • healthcare informatics
  • medical informatics
  • nursing informatics
  • clinical informatics
  • biomedical informatics

Published Papers (4 papers)

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Research

Open AccessArticle
Building a Persuasive Virtual Dietitian
Informatics 2020, 7(3), 27; https://doi.org/10.3390/informatics7030027 - 30 Jul 2020
Abstract
This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and [...] Read more.
This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results. Full article
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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Open AccessFeature PaperArticle
Investigation of Women’s Health on Wikipedia—A Temporal Analysis of Women’s Health Topic
Informatics 2020, 7(3), 22; https://doi.org/10.3390/informatics7030022 - 17 Jul 2020
Abstract
New health-related concepts, terms, and topics emerge, and the meanings of existing terms and topics keep changing. This study investigated and explored the evolutions of the women’s health topic on Wikipedia. The creation time, page views data, page edits data, and text of [...] Read more.
New health-related concepts, terms, and topics emerge, and the meanings of existing terms and topics keep changing. This study investigated and explored the evolutions of the women’s health topic on Wikipedia. The creation time, page views data, page edits data, and text of historical versions of 207 women-health-related entries from 2010 to 2017 on Wikipedia were collected. Coding, subject analysis, descriptive and inferential statistical analysis, and Self-Organizing Map and n-gram approaches were employed to explore the characteristics and evolutions of the entries for the women’s health topic. The results show that the number of the women-health-related entries kept increasing from 2010 to 2017, and nearly half of them were related to the supports and protection of women’s health. The total number of page views of the investigated items increased from 2011 to 2013, but it decreased from 2013 to 2017, while the total number of page edits stayed stable from 2010 to 2017. Growing subjects were found during the investigated period, such as abuse and violence, and family planning and reproduction. However, the entries related to the economy and politics were diminishing. There was no association between the internal characteristic evolution and the external popularity evolution of the women’s health topic. Full article
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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Open AccessArticle
Machine Learning for Identifying Medication-Associated Acute Kidney Injury
Informatics 2020, 7(2), 18; https://doi.org/10.3390/informatics7020018 - 31 May 2020
Cited by 1
Abstract
One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, [...] Read more.
One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, these studies are limited to assessing the impact of known nephrotoxic medications and do not comprehensively explore the relationship between medication combinations and AKI. In this paper, we present a population-based retrospective cohort study that employs automated data analysis techniques to identify medications and medication combinations that are associated with a higher risk of AKI. By integrating multivariable logistic regression, frequent itemset mining, and stratified analysis, this study is designed to explore the complex relationships between medications and AKI in such a way that has never been attempted before. Through an analysis of prescription records of one million older patients stored in the healthcare administrative dataset at ICES (an independent, non-profit, world-leading research organization that uses population-based health and social data to produce knowledge on a broad range of healthcare issues), we identified 55 AKI-associated medications among 595 distinct medications and 78 AKI-associated medication combinations among 7748 frequent medication combinations. In addition, through a stratified analysis, we identified 37 cases where a particular medication was associated with increasing the risk of AKI when used with another medication. We have shown that our results are consistent with previous studies through consultation with a nephrologist and an electronic literature search. This research demonstrates how automated analysis techniques can be used to accomplish data-driven tasks using massive clinical datasets. Full article
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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Open AccessFeature PaperArticle
Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records
Informatics 2020, 7(2), 17; https://doi.org/10.3390/informatics7020017 - 27 May 2020
Cited by 1
Abstract
Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to [...] Read more.
Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs. Full article
(This article belongs to the Special Issue Feature Papers: Health Informatics)
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