Special Issue "Data-Driven Healthcare Tasks: Tools, Frameworks, and Techniques"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Dr. Kamran Sedig
Website
Guest Editor
Department of Computer Science, Faculty of Information and Media Studies, The University of Western Ontario, London, ON N6A 3K7, Canada
Interests: human-data interaction; interactive cognition; visual reasoning; interaction and interactivity design; data and information visualization; information presentation and design; data analytics; visual interface design; task and activity analysis and design
Dr. Daniel J. Lizotte
Website
Guest Editor
Computer Science|Epidemiology & Biostatistics, The University of Western Ontario, London, ON N6A 5B7, Canada
Interests: machine learning; reinforcement learning; statistics; biostatistics

Special Issue Information

Dear Colleagues,

Technological advances have resulted in increased data collection, digitization, and storage of health data. This data is derived both from traditional sources like electronic medical records, genomic data, and clinical data, as well as from novel patient-generated sources like activity monitoring and social media. Health data is often big data. It has high volume, low veracity, great variety, and high velocity. Health data, when used properly, can revolutionize healthcare activities. It can improve productivity, eliminate waste, and support a broad range of tasks related to disease surveillance, patient care, research, and population health management. However, health data’s impact is contingent on the availability of tools that can help derive meaning from it. To date, the healthcare field lags behind other fields in the development of computational tools that support complex healthcare tasks.

This Special Issue invites research papers (both experimental and conceptual) that advance our understanding of tools, frameworks, and techniques that improve and support the performance of complex, data-driven healthcare tasks and activities. Topics include are but are not limited to the following areas:

  • Task analysis and design in healthcare;
  • Human–data interaction involving health data;
  • Machine learning for health data;
  • Human-centered health data analytics;
  • Visual analytics to improve healthcare;
  • Interactive machine learning and explainable AI.

Dr. Kamran Sedig
Dr. Daniel J. Lizotte
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 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. Data 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.

Published Papers (3 papers)

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Research

Open AccessArticle
U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures
Data 2020, 5(2), 37; https://doi.org/10.3390/data5020037 - 14 Apr 2020
Cited by 2
Abstract
Measurement of corneal nerve tortuosity is associated with dry eye disease, diabetic retinopathy, and a range of other conditions. However, clinicians measure tortuosity on very different grading scales that are inherently subjective. Using in vivo confocal microscopy, 253 images of corneal nerves were [...] Read more.
Measurement of corneal nerve tortuosity is associated with dry eye disease, diabetic retinopathy, and a range of other conditions. However, clinicians measure tortuosity on very different grading scales that are inherently subjective. Using in vivo confocal microscopy, 253 images of corneal nerves were captured and manually labelled by two researchers with tortuosity measurements ranging on a scale from 0.1 to 1.0. Tortuosity was estimated computationally by extracting a binarised nerve structure utilising a previously published method. A novel U-Net segmented adjacent angle detection (USAAD) method was developed by training a U-Net with a series of back feeding processed images and nerve structure vectorizations. Angles between all vectors and segments were measured and used for training and predicting tortuosity measured by human labelling. Despite the disagreement among clinicians on tortuosity labelling measures, the optimised grading measurement was significantly correlated with our USAAD angle measurements. We identified the nerve interval lengths that optimised the correlation of tortuosity estimates with human grading. We also show the merit of our proposed method with respect to other baseline methods that provide a single estimate of tortuosity. The real benefit of USAAD in future will be to provide comprehensive structural information about variations in nerve orientation for potential use as a clinical measure of the presence of disease and its progression. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Tasks: Tools, Frameworks, and Techniques)
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Open AccessArticle
The Eminence of Co-Expressed Ties in Schizophrenia Network Communities
Data 2019, 4(4), 149; https://doi.org/10.3390/data4040149 - 29 Nov 2019
Abstract
Exploring gene networks is crucial for identifying significant biological interactions occurring in a disease condition. These interactions can be acknowledged by modeling the tie structure of networks. Such tie orientations are often detected within embedded community structures. However, most of the prevailing community [...] Read more.
Exploring gene networks is crucial for identifying significant biological interactions occurring in a disease condition. These interactions can be acknowledged by modeling the tie structure of networks. Such tie orientations are often detected within embedded community structures. However, most of the prevailing community detection modules are intended to capture information from nodes and its attributes, usually ignoring the ties. In this study, a modularity maximization algorithm is proposed based on nonlinear representation of local tangent space alignment (LTSA). Initially, the tangent coordinates are computed locally to identify k-nearest neighbors across the genes. These local neighbors are further optimized by generating a nonlinear network embedding function for detecting gene communities based on eigenvector decomposition. Experimental results suggest that this algorithm detects gene modules with a better modularity index of 0.9256, compared to other traditional community detection algorithms. Furthermore, co-expressed genes across these communities are identified by discovering the characteristic tie structures. These detected ties are known to have substantial biological influence in the progression of schizophrenia, thereby signifying the influence of tie patterns in biological networks. This technique can be extended logically on other diseases networks for detecting substantial gene “hotspots”. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Tasks: Tools, Frameworks, and Techniques)
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Open AccessArticle
Catastrophic Household Expenditure for Healthcare in Turkey: Clustering Analysis of Categorical Data
Data 2019, 4(3), 112; https://doi.org/10.3390/data4030112 - 29 Jul 2019
Abstract
The amount of health expenditure at the household level is one of the most basic indicators of development in countries. In many countries, health expenditure increases relative to national income. If out-of-pocket health spending is higher than the income or too high, this [...] Read more.
The amount of health expenditure at the household level is one of the most basic indicators of development in countries. In many countries, health expenditure increases relative to national income. If out-of-pocket health spending is higher than the income or too high, this indicates an economical alarm that causes a lower life standard, called catastrophic health expenditure. Catastrophic expenditure may be affected by many factors such as household type, property status, smoking and drinking alcohol habits, being active in sports, and having private health insurance. The study aims to investigate households with respect to catastrophic health expenditure by the clustering method. Clustering enables one to see the main similarity and difference between the groups. The results show that there are significant and interesting differences between the five groups. C4 households earn more but spend less money on health problems by the rate of 3.10% because people who do physical exercises regularly have fewer health problems. A household with a family with one adult, landlord and three people in total (mother or father and two children) in the cluster C5 earns much money and spends large amounts for health expenses than other clusters. C1 households with elementary families with three children, and who do not pay rent although they are not landlords have the highest catastrophic health expenditure. Households in C3 have a rate of 3.83% health expenditure rate on average, which is higher than other clusters. Households in the cluster C2 make the most catastrophic health expenditure. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Tasks: Tools, Frameworks, and Techniques)
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