2021年3月19日晚7:30-9:00，AI TIME特别邀请了三位优秀的讲者跟大家共同开启AAAI专场六！正值三月，微风和煦，适合漫步。由AI TIME主办，并行科技为本期PhD分享赞助了丰厚的福利。AI TIME是清华大学计算机系一群关注人工智能发展，并有思想情怀的青年学者们创办的圈子。
AI TIME旨在发扬科学思辨精神，邀请各界人士对人工智能理论、算法、场景、应用的本质问题进行探索，加强思想碰撞，为大家打造一个知识分享的聚集地。本期直播邀请嘉宾但婷婷：华南理工大学计算机科学与技术专业博士研究生，研究方向：医学图像处理，fMRI分析。报告题目：面对突如其来的新冠，“AI”可以做点什么？
摘要：Coronavirus Disease 2019 (COVID-19) causes a sudden turn over to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent and large needs of ICUs posed great risks to the medical system. Estimating the mortality of critical in-patients who were not admitted into the ICU will be valuable to optimize the management and assignment of ICU. Retrospective, 733 in-patients diagnosed with COVID-19 at a local hospital (Wuhan, China), as of March 18, 2020. Demographic, clinical and laboratory results were collected and analyzed using machine learning to build a predictive model. Considering the shortage of ICU beds at the beginning of disease emergence, we defined the mortality for those patients who were predicted to be in needing of ICU care yet they did not as Missing-ICU (MI) -mortality. To estimate MI-mortality, a prognostic classification model was built to identify the in-patients who may need ICU care. Its predictive accuracy was 0.8288, with an AUC of 0.9119. On our cohort of 733 patients, 25 in-patients who have been predicted by our model that they should need ICU, yet they did not enter ICU due to lack of shorting ICU wards. Our analysis had shown that the MI-mortality is 41%, yet the mortality of ICU is 32%, implying that enough bed of ICU in treating patients in critical conditions.