首页|Tiangong University Reports Findings in Machine Learning (Intelligent alert syst em for predicting invasive mechanical ventilation needs via noninvasive paramete rs: employing an integrated machine learning method with integration of multicen ter ...)
Tiangong University Reports Findings in Machine Learning (Intelligent alert syst em for predicting invasive mechanical ventilation needs via noninvasive paramete rs: employing an integrated machine learning method with integration of multicen ter ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Tianjin, People's Republ ic of China, by NewsRx journalists, research stated, "The use of invasive mechan ical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunct ion. Accurately predicting the demand for IMV is vital for clinical decision-mak ing." The news correspondents obtained a quote from the research from Tiangong Univers ity, "However, current techniques are invasive and challenging to implement in p re-hospital and emergency rescue settings. To address this issue, a real-time pr ediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achiev ing an AUC value of 0.935 (95 % CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpass ing the performance of traditional risk adjustment algorithms (OSI(oxygenation s aturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the mode l's decision-making. These findings highlight the substantial potential of a mac hine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand."
TianjinPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesHospitalsMachine Learning
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