Author: Aronis, John M.; Ferraro, Jeffrey P.; Gesteland, Per H.; Tsui, Fuchiang; Ye, Ye; Wagner, Michael M.; Cooper, Gregory F.
Title: A Bayesian approach for detecting a disease that is not being modeled Document date: 2020_2_28
ID: 0xbozygd_18
Snippet: That is, θB,f is the probability that a patient with disease B has finding f, and θU,f is the probability that a patient with disease U has finding f. We estimate each θB,f with:
θB,f=(#fb+1)/(#totalb+2)(5)
where #fb is the number of patients in the baseline window with finding f and #totalb is the total number of patients in the baseline window. This estimate is based on our assumption that the baseline window includes only patients with bac.....
Document: That is, θB,f is the probability that a patient with disease B has finding f, and θU,f is the probability that a patient with disease U has finding f. We estimate each θB,f with:
θB,f=(#fb+1)/(#totalb+2)(5)
where #fb is the number of patients in the baseline window with finding f and #totalb is the total number of patients in the baseline window. This estimate is based on our assumption that the baseline window includes only patients with background ILI. Also, let #fm be the number of patients in the monitor window with finding f, and let {θB,f}, {θU,f}, and {#fm} denote the sets of these values where f ranges over the set of findings.
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