  We develop here a data-driven approach for disease recognition based on given
symptoms, to be efficient tool for anomaly detection. In a clinical setting and
when presented with a patient with a combination of traits, a doctor may wonder
if a certain combination of symptoms may be especially predictive, such as the
question, "Are fevers more informative in women than men?" The answer to this
question is, yes. We develop here a methodology to enumerate such questions, to
learn what are the stronger warning signs when attempting to diagnose a
disease, called Conditional Predictive Informativity, (CPI), whose ranking we
call CPIR. This simple to use process allows us to identify particularly
informative combinations of symptoms and traits that may help medical field
analysis in general, and possibly to become a new data-driven advised approach
for individual medical diagnosis, as well as for broader public policy
discussion. In particular we have been motivated to develop this tool in the
current enviroment of the pressing world crisis due to the COVID 19 pandemic.
We apply the methods here to data collected from national, provincial, and
municipal health reports, as well as additional information from online, and
then curated to an online publically available Github repository.
