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Diagnostics
Special Issues
Mobile Health, Machine Learning, and Diagnostics for Infectious Diseases
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Mobile Health, Machine Learning, and Diagnostics for Infectious Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Diagnostic Microbiology and Infectious Disease".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1007

Special Issue Editors

Dr. Thomas Foster Scherr

E-Mail Website
Guest Editor
Department of Chemistry, Vanderbilt University, 1234 Stevenson Center Lane, Nashville, TN 37212, USA
Interests: mobile health; diagnostics; disease surveillance; immunoassays; data science
Dr. David W. Wright

E-Mail Website
Guest Editor
Department of Chemistry, Vanderbilt University, 1234 Stevenson Center Lane, Nashville, TN 37212, USA
Interests: immunoassays; point-of-care diagnostics; low-resource setting diagnostics; global health; neglected tropical diseases
Dr. Carson Paige Moore

E-Mail Website
Guest Editor
Department of Chemistry, Vanderbilt University, 1234 Stevenson Center Lane, Nashville, TN 37212, USA
Interests: infectious disease epidemiology and surveillance; diagnostic design, development, and implementation; mobile health; mobile phone applications for disease surveillance and contact tracing; infectious diseases of poverty; neglected tropical diseases

Special Issue Information

Dear Colleagues,

Infectious disease diagnosis is an interdisciplinary endeavor that unites clinical expertise with technology, reflecting the diverse landscapes of case management and the range of healthcare settings. This is a unique and opportune moment for us to harness the widespread adoption of mobile phones, the growing utilization of information technologies, and the rapid advances in data science and artificial intelligence. These advancements enable for the development of innovative diagnostic platforms and approaches that can potentially shift the point of care away from high-resource facilities, enhance diagnostic performance, streamline workflows, integrate seamlessly with electronic records, and enable predictive epidemiology. We find ourselves in an exciting period filled with numerous opportunities for improvements which will lead to better patient satisfaction, more precise disease surveillance, and enhanced technical aspects of diagnostic assay performance and form factor.

We are pleased to announce this Special Issue titled "Mobile Health, Machine Learning, and Diagnostics for Infectious Diseases". On behalf of the Diagnostics editorial office, we welcome you to contribute by submitting original research and review articles that explore new approaches in mobile health, data science, devices, or assays to advance infectious disease diagnostics.

We hope that you may join us in contributing and become a part of driving progress in the field of infectious disease diagnostics.

Dr. Thomas Foster Scherr
Dr. David W. Wright
Dr. Carson Paige Moore
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. Diagnostics 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 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

  • infectious diseases
  • mobile health
  • machine learning
  • epidemiology
  • disease surveillance

Published Papers (1 paper)

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Research

21 pages, 1615 KiB  
Article
Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease
by Thomas F. Scherr, Christina E. Douglas, Kurt E. Schaecher, Randal J. Schoepp, Keersten M. Ricks and Charles J. Shoemaker
Diagnostics 2024, 14(12), 1290; https://doi.org/10.3390/diagnostics14121290 - 18 Jun 2024
Viewed by 349
Abstract
In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning [...] Read more.
In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states. Full article
(This article belongs to the Special Issue Mobile Health, Machine Learning, and Diagnostics for Infectious Diseases)
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