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Explainable AI Enabled Infant Mortality Prediction Based on Neonatal Sepsis
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作者 Priti Shaw Kaustubh Pachpor Suresh Sankaranarayanan 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期311-325,共15页
Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem,especially in developing countries.There have been researches on human sepsis,vaccine response,and immunity.Also... Neonatal sepsis is the third most common cause of neonatal mortality and a serious public health problem,especially in developing countries.There have been researches on human sepsis,vaccine response,and immunity.Also,machine learning methodologies were used for predicting infant mortality based on certain features like age,birth weight,gestational weeks,and Appearance,Pulse,Grimace,Activity and Respiration(APGAR)score.Sepsis,which is considered the most determining condition towards infant mortality,has never been considered for mortality prediction.So,we have deployed a deep neural model which is the state of art and performed a comparative analysis of machine learning models to predict the mortality among infants based on the most important features including sepsis.Also,for assessing the prediction reliability of deep neural model which is a black box,Explainable AI models like Dalex and Lime have been deployed.This would help any non-technical personnel like doctors and practitioners to understand and accordingly make decisions. 展开更多
关键词 APGAR SEPSIS explainable AI machine learning
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UTR-CTOE: A New Paradigm Explaining CAATs Adoption
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作者 Mootooganagen Ramen Bhavish Jugumath Prachitee Ramhit 《Journal of Modern Accounting and Auditing》 2015年第12期615-631,共17页
关键词 电信运营企业 审计技术 计算机辅助 组织文化 中国 组成 环境因素 技术组织
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