lassa_randomized_data's People
lassa_randomized_data's Issues
Save output from notebooks to output folder
Save tables as csv files and images as pdf
Calculate number of days a pre-admission symptom is present
In the current version of the binary analysis script, 1 is assigned when the symptom variable is not empty, 0 otherwise:
https://github.com/colabobio/Lassa_randomized_data/blob/main/binary_variable_analysis.Rmd#L44
However, this does not differentiate between the situation when the symptom was present only a one of a few days versus a for many days. This could be done easily by counting how many different words Dx are in the string.
Implement model validation
The prognostic model presented in the 2018 TLID paper is a very simply logistic regression model with the following predictors:
Intercept = -8.540674959
Age = 0.043498988
Severe CNS = 1.011961038
Bleeding = 0.898388975
Jaundice = 2.029453703
AST = 0.002990266
Cr = 0.146072204
K = 0.923083855
We need to apply this model on the new dataset for independent validation.
Calculate the binary var statistics at presentation
This is what we did in the TLID 2018 paper, which means they have to have "d1"
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