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Challenges of Health Data Analytics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 3787

Special Issue Editor


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Guest Editor

Special Issue Information

This Special Issue invites papers accepted to Data Science in Health, a Special Session from the IEEE DSAA'2021 which will be held in Porto, Portugal, 6–9 October 2021. Additionally, the web page can be found below:

https://medal.ctb.upm.es/dsaa21_dshealth/index.html

Topics addressing the uncertainty of machine learning methods, the explainability of back-box models, interoperability, federated databases, and the integration of sources spanning the life of the patient are especially welcome, although we also welcome contributions from the wider domain of medical information processing. The goal of this session is to familiarize the health sector with the ways in which artificial intelligence (AI) can help the health sector, and to frame data mining research in the context of what medical researchers can expect from their data.

Dr. Alejandro Rodríguez González
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. Entropy is an international peer-reviewed open access monthly 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 2600 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 data analysis
  • explainable AI
  • statistics
  • machine learning
  • federated analysis

Published Papers (1 paper)

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Research

26 pages, 2111 KiB  
Article
Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users
by Subash Prakash, Vishnu Unnikrishnan, Rüdiger Pryss, Robin Kraft, Johannes Schobel, Ronny Hannemann, Berthold Langguth, Winfried Schlee and Myra Spiliopoulou
Entropy 2021, 23(12), 1695; https://doi.org/10.3390/e23121695 - 17 Dec 2021
Cited by 1 | Viewed by 3048
Abstract
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users’ condition changes, but appropriate learning and visualization mechanisms are required for this purpose. [...] Read more.
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users’ condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported. Full article
(This article belongs to the Special Issue Challenges of Health Data Analytics)
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