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Predictive model of risk factors of High Flow Nasal Cannula using machine learning in COVID-19

Infectious Disease Modelling 2022年第3期7卷 526-534页

作者： Nobuaki Matsunaga Keisuke Kamata Yusuke Asai Shinya Tsuzuki Yasuaki Sakamoto shinpei ijichi Takayuki Akiyama a Jiefu Yu Gen Yamada Mari Terada Setsuko Suzuki Kumiko Suzuki Sho Saito Kayoko Hayakawa Norio Ohmagari AMR Clinical Reference Center National Center for Global Health and MedicineTokyoJapan DataRobot Inc. TokyoJapan Disease Control and Prevention Center National Center for Global Health and MedicineTokyoJapan Biostatistics Division Center for Research Administration and SupportNational Cancer CenterTokyoJapan

With the rapid increase in the number of COVID-19 patients in Japan,the number of patients receiving oxygen at home has also increased rapidly,and some of these patients have *** efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19,and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical *** admitted to medical institutions in Japan from November 14,2020 to April 11,2021 and registered in the COVID-19 Registry Japan were *** factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine ***-specific cohorts were created,and severity prediction was performed for the patient surge *** were able to obtain a model that was able to predict severe disease with a sensitivity of 57%when the specificity was set at 90%for those aged 40e59 years,and with a specificity of 50%and 43%when the sensitivity was set at 90%for those aged 60e79 years and 80 years and older,*** were able to identify lactate dehydrogenase level(LDH)as an important factor in predicting the severity of illness in all age *** machine learning,we were able to identify risk factors with high accuracy,and predict the severity of the *** plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.

关键词： COVID-19 Machine learning Severity Risk prediction Japan

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