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: closed (31 October 2024) | Viewed by 3202
Special Issue Editors
Interests: machine learning; biomedical engineering
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
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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
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