Infection prediction using physiological and social data in social environments
作者：
Highlights：
• Infection diagnosis is critical in social environments; otherwise diseases may spread fast.
• Clinical decision support system was developed for infection diagnosis in nursing homes using small data; AUROC of 0.734 achieved.
• In small data environments, external sources of data can improve diagnosis performance with little cost.
• When considering additional data (social data and weather information), AUROC increases up to 0.798.
• For diagnosis, smaller leads (historical data) is preferred, but larger leads improve the results when adding social data.
摘要
•Infection diagnosis is critical in social environments; otherwise diseases may spread fast.•Clinical decision support system was developed for infection diagnosis in nursing homes using small data; AUROC of 0.734 achieved.•In small data environments, external sources of data can improve diagnosis performance with little cost.•When considering additional data (social data and weather information), AUROC increases up to 0.798.•For diagnosis, smaller leads (historical data) is preferred, but larger leads improve the results when adding social data.
论文关键词：Infection diagnosis,Clinical decision support system,Machine learning,Physiological signals,Social data,68T10,92C50,97R40
论文评审过程：Received 20 November 2019, Revised 9 January 2020, Accepted 21 January 2020, Available online 29 January 2020, Version of Record 29 January 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102213
