Selected article for: "discharge patient and illness onset"
Author: zhuo wang; John S. Ji; Yang Liu; Runyou Liu; Yuxin Zha; Xiaoyu Chang; Lun Zhang; Yu Zhang; Jing Zeng; Ting Dong; Xinyin Xu; Lijun Zhou; Jun He; Yin Deng; Bo Zhong; Xianping Wu
Title: Survival analysis of hospital length of stay of novel coronavirus (COVID-19) pneumonia patients in Sichuan, China
  • Document date: 2020_4_10
    • ID: 3xfdiz26_15
    Snippet: Where h(t) is the discharge hazard at stay length t for a patient with a set of predictors X 1 , X 2 …; h 0 (t) is the baseline discharge hazard function; covariates X were the patient's age, gender, time interval from illness onset to diagnosis, hospital grade of patients, health servicers per 1,000 population in the patient's permanent residence, clinical grade, and β 1 , β 2 … are the model parameters describing the effect of the predict.....
    Document: Where h(t) is the discharge hazard at stay length t for a patient with a set of predictors X 1 , X 2 …; h 0 (t) is the baseline discharge hazard function; covariates X were the patient's age, gender, time interval from illness onset to diagnosis, hospital grade of patients, health servicers per 1,000 population in the patient's permanent residence, clinical grade, and β 1 , β 2 … are the model parameters describing the effect of the predictors on the overall hazard. We transformed continuous (age, time interval from illness onset to diagnosis, health servicers per 1,000 population) and multilevel variables (hospital grade of patients, clinical grade) into binary variables according to the variables' distribution. Statistical analyses were conducted in R (version 3.6.3), and p-values less than 0.05 for parameter estimates were considered statistically significant.

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