predictive model of covid19

Inspiration

My idea is to adapt an existing model for predictive maintenance. the following is a starting point for discussion, ideas, collaboration and possibly project work.

Please share your comments - joepareti54@gmail.com

Goal Classify patients as healthy vs. probability of being infected

Value Help healthcare planning, ICU planning

Data

Patient data, health records, symptoms, mortality, relevant
pathologies (e.g. blood pressure, diabetes)
Patient whereabouts

Classification Healthy vs. probability of requiring ICU within x hours

Leverage common features:

Time-delay features: time series of observations leading to a critical case or death
symptoms, vitals (e.g. body temperature, etc) similar to ‘telemetry data’
number of days a person has been in danger areas

supervised ML model.

Labeling strategy: label as failed all cases within a time window in advance of failure. In the case of coronavirus the time window could be chosen as ‘sufficient lead time’ for the ICU

Algorithms random forests or other ML algorithms (adaboost?) maximize recall because the real goal is to reduce false negatives