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Diagnostics
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Computerized Decision Support Systems for Lung Ventilation
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Computerized Decision Support Systems for Lung Ventilation

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: closed (31 August 2023) | Viewed by 3440

Special Issue Editor

Prof. Dr. Fleur T. Tehrani

E-Mail Website
Guest Editor
College of Engineering and Computer Science, California State University, Fullerton, CA, USA
Interests: biomedical engineering; clinical engineering; respiratory assist devices; mechanical ventilation; systems control; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The application of artificial intelligence and computerized decision support systems is increasing in critical care. As the amount of patient data increases, artificial intelligence systems become valuable tools for clinicians to provide optimal treatments to patients under their care. One of the most important areas of critical care is mechanical ventilation. The focus of this Special Issue is on computerized decision support systems for lung ventilation. Papers on invasive and non-invasive positive pressure ventilation are preferred, but other types of mechanical ventilation will also be considered. Papers should describe original research on new systems, new case studies, or applications of systems, reviews, or short communications, and should not have been previously published or concurrently submitted to other journals. Detailed descriptions of previously published conference presentations will be considered.

Prof. Dr. Fleur T. Tehrani
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. 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.

Published Papers (2 papers)

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Research

21 pages, 5991 KiB  
Article
Self-Regulating Adaptive Controller for Oxygen Support to Severe Respiratory Distress Patients and Human Respiratory System Modeling
by Indrajit Naskar, Arabinda Kumar Pal and Nandan Kumar Jana
Diagnostics 2023, 13(5), 967; https://doi.org/10.3390/diagnostics13050967 - 03 Mar 2023
Viewed by 1343
Abstract
Uncontrolled breathing is the most critical and challenging situation for a healthcare person to patients. It may be due to simple cough/cold/critical disease to severe respiratory infection of the patients and resulting directly impacts the lungs and damages the alveoli which leads to [...] Read more.
Uncontrolled breathing is the most critical and challenging situation for a healthcare person to patients. It may be due to simple cough/cold/critical disease to severe respiratory infection of the patients and resulting directly impacts the lungs and damages the alveoli which leads to shortness of breath and also impairs the oxygen exchange. The prolonged respiratory failure in such patients may cause death. In this condition, supportive care of the patients by medicine and a controlled oxygen supply is only the emergency treatment. In this paper, as a part of emergency support, the intelligent set-point modulated fuzzy PI-based model reference adaptive controller (SFPIMRAC) is delineated to control the oxygen supply to uncomforted breathing or respiratory infected patients. The effectiveness of the model reference adaptive controller (MRAC) is enhanced by assimilating the worthiness of fuzzy-based tuning and set-point modulation strategies. Since then, different conventional and intelligent controllers have attempted to regulate the supply of oxygen to respiratory distress patients. To overcome the limitations of previous techniques, researchers created the set-point modulated fuzzy PI-based model reference adaptive controller, which can react instantly to changes in oxygen demand in patients. Nonlinear mathematical formulations of the respiratory system and the exchange of oxygen with time delay are modeled and simulated for study. The efficacy of the proposed SFPIMRAC is tested, with transport delay and set-point variations in the devised respiratory model. Full article
(This article belongs to the Special Issue Computerized Decision Support Systems for Lung Ventilation)
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33 pages, 2624 KiB  
Article
A Novel Strategy to Fit and Validate Physiological Models: A Case Study of a Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise
by Carlos A. Sarmiento, Leidy Y. Serna, Alher M. Hernández and Miguel Á. Mañanas
Diagnostics 2023, 13(5), 908; https://doi.org/10.3390/diagnostics13050908 - 27 Feb 2023
Viewed by 1420
Abstract
Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is [...] Read more.
Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy’s usefulness. Full article
(This article belongs to the Special Issue Computerized Decision Support Systems for Lung Ventilation)
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