%0 Generic %A Wu, Qinhao %A Ye, Fei %A Gu, Qianqian %A Xiao, Quan %D 2024 %T FHC dataset underlying the publication: A Customised Down-sampling Machine Learning Approach for Sepsis Prediction %U %R 10.4121/02c23622-17b5-40c8-909d-7ac5d1387cb7.v1 %K Sepsis %K Intensive Care Unit %K Alarm Reduction %K Early Prediction %K Machine Learning %X

The FHC dataset was collected from patients 18 years or older from the First Hospital of Changsha, China, between 2020 and 2022. The collected data contained laboratory test values and vital signs from adult ICU patients, including 69 sepsis cases and 46 non-septic cardiovascular-disease-only cases. The laboratory tests were conducted following the routine of clinical practice. The laboratory results were collected on a daily basis. The laboratory instruments and measurements are listed in the supplementary material. The items of daily laboratory tests were used as data features. The sepsis label was given by the intensivist's suspicion of onset time following the Sepsis-3 clinical criteria. Features included in the FHC dataset contain 5 vital signs, 31 laboratory values and 4 demographic information. Vital signs collected manually by nurses include temperature (Temp), heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP).  


The laboratory routine for gathering the measurement is stated as follows: 


This study was conducted in accordance with the principles of the Declaration of Helsinki, and the study protocol was approved by the Ethics Review Committee of the First Hospital of Changsha ((2023) Ethic [Clinical paper] No. 1). Because of the retrospective nature of the study, patient consent for inclusion was waived.

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