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Diagnostics
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Diagnosing Respiratory Diseases and Impaired Gas Exchange Using Machine Learning
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Diagnosing Respiratory Diseases and Impaired Gas Exchange Using Machine Learning

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1495

Special Issue Editors

Dr. Robert LeMoyne

E-Mail Website
Guest Editor
Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 27201 Kladno, Czech Republic
Interests: machine learning; biomedical engineering
Dr. Jakub Rafl

E-Mail Website
Guest Editor
Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 27201 Kladno, Czech Republic
Interests: machine learning; biomedical engineering
Dr. Martin Rozanek

E-Mail Website
Guest Editor
Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 27201 Kladno, Czech Republic
Interests: machine learning; biomedical engineering

Special Issue Information

Dear Colleagues,

Recognition, quantification, and diagnosis of respiratory diseases and impaired gas exchange is essential in hospitals and clinics and offers great potential outside medical environments. Sensors that assess the adequacy of oxygenation and ventilation are readily available, and the use of the pulse oximeter is already ubiquitous. Still, a clinical assessment always requires expert interpretation. Although closed-loop control systems are becoming available, they still require supervision. Outside the clinical environment their use is primarily a novelty. With the advent of machine learning, these sensors can obtain signal data that can be synthesized for machine learning applications to provide augmented clinical interpretation and also to offer enhanced situational awareness.

This Special Issue invites research manuscripts that explore the integration of machine learning with blood gas sensors in evolving the state of the art of patient assessment, worker assessment and personal health. Further, manuscripts that explore demonstrations of concept to verifications of robust systems are encouraged. Finally, evaluation of existing expert systems with a discussion of enhancements via the use of machine learning are also welcome for consideration.

Dr. Robert LeMoyne
Dr. Jakub Rafl
Dr. Martin Rozanek
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

  • machine learning
  • user trust with machine learning
  • deep learning
  • blood gas sensors
  • oximeters
  • wearables
  • computer automated diagnosis of health status
  • respiratory diseases
  • impaired gas exchange
  • advanced diagnostics

Published Papers (2 papers)

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Research

15 pages, 2935 KiB  
Article
A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm
by Zne-Jung Lee, Ming-Ren Yang and Bor-Jiunn Hwang
Diagnostics 2024, 14(7), 723; https://doi.org/10.3390/diagnostics14070723 - 29 Mar 2024
Viewed by 518
Abstract
Asthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic [...] Read more.
Asthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic losses due to the degradation of patients’ quality of life and the impairment of their physical fitness. Asthma research has evolved in recent years to fully analyze why certain diseases develop based on a variety of data and observations of patients’ performance. The advent of new techniques offers good opportunities and application prospects for the development of asthma diagnosis methods. Over the last few decades, techniques like data mining and machine learning have been utilized to diagnose asthma. Nevertheless, these traditional methods are unable to address all of the difficulties associated with improving a small dataset to increase its quantity, quality, and feature space complexity at the same time. In this study, we propose a sustainable approach to asthma diagnosis using advanced machine learning techniques. To be more specific, we use feature selection to find the most important features, data augmentation to improve the dataset’s resilience, and the extreme gradient boosting algorithm for classification. Data augmentation in the proposed method involves generating synthetic samples to increase the size of the training dataset, which is then utilized to enhance the training data initially. This could lessen the phenomenon of imbalanced data related to asthma. Then, to improve diagnosis accuracy and prioritize significant features, the extreme gradient boosting technique is used. The outcomes indicate that the proposed approach performs better in terms of diagnostic accuracy than current techniques. Furthermore, five essential features are extracted to help physicians diagnose asthma. Full article
(This article belongs to the Special Issue Diagnosing Respiratory Diseases and Impaired Gas Exchange Using Machine Learning)
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10 pages, 579 KiB  
Article
Determining an Optimal Oxygen Saturation Target Range Based on Neonatal Maturity: Demonstration of a Decision Tree Analytic
by Thomas E. Bachman, Narayan P. Iyer, Christopher J. L. Newth and Robert LeMoyne
Diagnostics 2023, 13(21), 3312; https://doi.org/10.3390/diagnostics13213312 - 26 Oct 2023
Viewed by 695
Abstract
The utility of decision tree machine learning in exploring the interactions among the SpO2 target range, neonatal maturity, and oxemic-risk is demonstrated. METHODS: This observational study used 3 years of paired age-SpO2-PaO2 data from a neonatal ICU. The CHAID [...] Read more.
The utility of decision tree machine learning in exploring the interactions among the SpO2 target range, neonatal maturity, and oxemic-risk is demonstrated. METHODS: This observational study used 3 years of paired age-SpO2-PaO2 data from a neonatal ICU. The CHAID decision tree method was used to explore the interaction of postmenstrual age (PMA) on the risk of extreme arterial oxygen levels at six different potential SpO2 target ranges (88–92%, 89–93%, 90–94%, 91–95%, 92–96% and 93–97%). Risk was calculated using a severity-weighted average of arterial oxygen outside the normal range for neonates (50–80 mmHg). RESULTS: In total, 7500 paired data points within the potential target range envelope were analyzed. The two lowest target ranges were associated with the highest risk, and the ranges of 91–95% and 92–96% were associated with the lowest risk. There were shifts in the risk associated with PMA. All the target ranges showed the lowest risk at ≥42 weeks PMA. The lowest risk for preterm infants was within a target range of 92–96% with a PMA of ≤34 weeks. CONCLUSIONS: This study demonstrates the utility of decision tree analytics. These results suggest that SpO2 target ranges that are different from typical range might reduce morbidity and mortality. Further research, including prospective randomized trials, is warranted. Full article
(This article belongs to the Special Issue Diagnosing Respiratory Diseases and Impaired Gas Exchange Using Machine Learning)
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