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Paper Type
Complete
Description
COVID-19 pandemic impaired function of healthcare systems globally by increased workload at intensive care units in hospitals that underwent a massive influx of patients in critical condition in need of tailored and specialized care. Prevalent respiratory failure increased the demand for ventilators and respiratory therapists. Due to personnel shortages, doctors and nurses, not trained in the field of intensive care, were expected to provide adequate care for patients, supporting themselves with help of consulting specialists and navigating equipment shortages. To deal with this problem we present the research result, conducted in cooperation between computer scientists and anesthesiologists, namely the advisory system that supports physicians who do not have expertise in operating ventilators for typical COVID-19 patients. The system has been implemented at two hospitals. We present the system architecture and some internals of its recommendation modules based on decision graphs and recurrent neural networks. Further research will embrace analysis and optimization of the modules to provide yet more accurate support, as early as possible and in a broader spectrum of patient’s conditions.
Paper Number
1799
Recommended Citation
Grąbczewski, Krzysztof; Adamczak, Rafał; Grochowski, Marek; Joachimiak, Michal; Sokolov, Oleksandr; Kołodziejczak, Michalina Marta; Kowalski, Piotr; and Sierakowska, Katarzyna, "Mechanical ventilation settings advisory system Odyn" (2023). AMCIS 2023 Proceedings. 22.
https://aisel.aisnet.org/amcis2023/sig_health/sig_health/22
Mechanical ventilation settings advisory system Odyn
COVID-19 pandemic impaired function of healthcare systems globally by increased workload at intensive care units in hospitals that underwent a massive influx of patients in critical condition in need of tailored and specialized care. Prevalent respiratory failure increased the demand for ventilators and respiratory therapists. Due to personnel shortages, doctors and nurses, not trained in the field of intensive care, were expected to provide adequate care for patients, supporting themselves with help of consulting specialists and navigating equipment shortages. To deal with this problem we present the research result, conducted in cooperation between computer scientists and anesthesiologists, namely the advisory system that supports physicians who do not have expertise in operating ventilators for typical COVID-19 patients. The system has been implemented at two hospitals. We present the system architecture and some internals of its recommendation modules based on decision graphs and recurrent neural networks. Further research will embrace analysis and optimization of the modules to provide yet more accurate support, as early as possible and in a broader spectrum of patient’s conditions.
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SIG Health