مؤتمر
Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning
| العنوان: | Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning |
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| المؤلفون: | Stell, A, Caparo, E, Wang, Z, Wang, C, Berlowitz, D, Howard, M, Sinnott, R, Aickelin, U |
| المصدر: | 17th International Conference on Health Informatics |
| بيانات النشر: | SCITEPRESS - Science and Technology Publications |
| سنة النشر: | 2024 |
| المجموعة: | The University of Melbourne: Digital Repository |
| الوصف: | Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial. |
| نوع الوثيقة: | conference object |
| اللغة: | unknown |
| ردمك: | 978-989-758-688-0 989-758-688-1 |
| تدمد: | 2184-4305 |
| العلاقة: | Stell, A., Caparo, E., Wang, Z., Wang, C., Berlowitz, D., Howard, M., Sinnott, R. & Aickelin, U. (2024). Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning. Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies Volume 2, pp.436-443. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012366700003657Test.; http://hdl.handle.net/11343/340538Test |
| الإتاحة: | https://doi.org/10.5220/0012366700003657Test http://hdl.handle.net/11343/340538Test |
| حقوق: | CC BY-NC-ND ; https://creativecommons.org/licenses/by-nc-nd/4.0Test |
| رقم الانضمام: | edsbas.73816B36 |
| قاعدة البيانات: | BASE |
| ردمك: | 9789897586880 9897586881 |
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| تدمد: | 21844305 |