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Relapse or reinfection: Classification of malaria infection using transition likelihoods
Author(s) -
Lin FengChang,
Li Quefeng,
Lin Jessica T.
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13226
Subject(s) - malaria , plasmodium vivax , classifier (uml) , machine learning , medicine , immunology , artificial intelligence , computer science , plasmodium falciparum
In patients with Plasmodium vivax malaria treated with effective blood‐stage therapy, the recurrent illness may occur due to relapse from latent liver‐stage infection or reinfection from a new mosquito bite. Classification of the recurrent infection as either relapse or reinfection is critical when evaluating the efficacy of an anti‐relapse treatment. Although one can use whether a shared genetic variant exists between baseline and recurrence genotypes to classify the outcome, little has been suggested to use both sharing and nonsharing variants to improve the classification accuracy. In this paper, we develop a novel classification criterion that utilizes transition likelihoods to distinguish relapse from reinfection. When tested in extensive simulation experiments with known outcomes, our classifier has superior operating characteristics. A real data set from 78 Cambodian P. vivax malaria patients was analyzed to demonstrate the practical use of our proposed method.

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