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Informatics
Special Issues
Health Informatics: Feature Review Papers
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Health Informatics: Feature Review Papers

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 4097

Special Issue Editors

Prof. Dr. Jiang Bian

E-Mail Website
Guest Editor
Biomedical Informatics, Department of Health Outcomes & Policy, College of Medicine, University of Florida, Gainesville, FL 32610, USA
Interests: real-world data; electronic health records; data science; machine learning; data privacy; security; clinical and clinical research informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Guo

E-Mail Website
Guest Editor
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA
Interests: electronic health records; data science; biostatistics; patient-reported outcomes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical and health informatics is an interdisciplinary field, where the central theme is to explore the effective uses of data, information, and knowledge for scientific inquiry, problem-solving, and decision-making, motivated by efforts to improve human health. Research in health informatics has always been data-centric, and advancements have heavily relied on the availability of various, heterogenous, and novel new data sources. For example, the rapid adoption of electronic health records (EHRs) in the past few decades has made large collections of real-world healthcare data available. Further, recent advancements in artificial intelligence, especially machine learning and deep learning, have enabled us to develop novel biomedical and health informatics methods and tools to address a wide range of applications in improving health and health equity.

This Special Issue invites review papers related to all aspects of health informatics: artificial intelligence and data science, electronic health records, curation of novel data sources, digital health, social determinants of health, causal inference, clinical trial design, etc.

Prof. Dr. Jiang Bian
Dr. Yi Guo
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. 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 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.

Keywords

  • health informatics
  • biomedical informatics
  • artificial intelligence
  • HER
  • health outcomes
  • health disparities
  • social determinants of health

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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23 pages, 2819 KiB  
Review
Machine Learning Applied to the Analysis of Prolonged COVID Symptoms: An Analytical Review
by Paola Patricia Ariza-Colpas, Marlon Alberto Piñeres-Melo, Miguel Alberto Urina-Triana, Ernesto Barceló-Martinez, Camilo Barceló-Castellanos and Fabian Roman
Informatics 2024, 11(3), 48; https://doi.org/10.3390/informatics11030048 - 18 Jul 2024
Viewed by 1463
Abstract
The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent [...] Read more.
The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent technologies such as machine learning have made great contributions to the understanding, identification, and treatment of the disease. Due to the sudden appearance of this virus, many works have been carried out by the scientific community to support the detection and treatment processes, which has generated numerous publications, making it difficult to identify the status of current research and future contributions that can continue to be generated around this problem that is still valid among us. To address this problem, this article shows the result of a scientometric analysis, which allows the identification of the various contributions that have been generated from the line of automatic learning for the monitoring and treatment of symptoms associated with this pathology. The methodology for the development of this analysis was carried out through the implementation of two phases: in the first phase, a scientometric analysis was carried out, where the countries, authors, and magazines with the greatest production associated with this subject can be identified, later in the second phase, the contributions based on the use of the Tree of Knowledge metaphor are identified. The main concepts identified in this review are related to symptoms, implemented algorithms, and the impact of applications. These results provide relevant information for researchers in the field in the search for new solutions or the application of existing ones for the treatment of still-existing symptoms of COVID-19. Full article
(This article belongs to the Special Issue Health Informatics: Feature Review Papers)
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Graphical abstract

15 pages, 1551 KiB  
Review
Variations in Using Diagnosis Codes for Defining Age-Related Macular Degeneration Cohorts
by Fritz Gerald Paguiligan Kalaw, Jimmy S. Chen and Sally L. Baxter
Informatics 2024, 11(2), 28; https://doi.org/10.3390/informatics11020028 - 1 May 2024
Viewed by 1764
Abstract
Data harmonization is vital for secondary electronic health record data analysis, especially when combining data from multiple sources. Currently, there is a gap in knowledge as to how studies identify cohorts of patients with age-related macular degeneration (AMD), a leading cause of blindness. [...] Read more.
Data harmonization is vital for secondary electronic health record data analysis, especially when combining data from multiple sources. Currently, there is a gap in knowledge as to how studies identify cohorts of patients with age-related macular degeneration (AMD), a leading cause of blindness. We hypothesize that there is variation in using medical condition codes to define cohorts of AMD patients that can lead to either the under- or overrepresentation of such cohorts. This study identified articles studying AMD using the International Classification of Diseases (ICD-9, ICD-9-CM, ICD-10, and ICD-10-CM). The data elements reviewed included the year of publication; dataset origin (Veterans Affairs, registry, national or commercial claims database, and institutional EHR); total number of subjects; and ICD codes used. A total of thirty-seven articles were reviewed. Six (16%) articles used cohort definitions from two ICD terminologies. The Medicare database was the most used dataset (14, 38%), and there was a noted increase in the use of other datasets in the last few years. We identified substantial variation in the use of ICD codes for AMD. For the studies that used ICD-10 terminologies, 7 (out of 9, 78%) defined the AMD codes correctly, whereas, for the studies that used ICD-9 and 9-CM terminologies, only 2 (out of 30, 7%) defined and utilized the appropriate AMD codes (p = 0.0001). Of the 43 cohort definitions used from 37 articles, 31 (72%) had missing or incomplete AMD codes used, and only 9 (21%) used the exact codes. Additionally, 13 articles (35%) captured ICD codes that were not within the scope of AMD diagnosis. Efforts to standardize data are needed to provide a reproducible research output. Full article
(This article belongs to the Special Issue Health Informatics: Feature Review Papers)
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