Create Black Box Optimization Control Systems Engineering Approach to Precision Medicine #118
Description
See
https://github.com/wishonia/FDAi/edit/develop/libs/black-box-optimization/README.md
☝️The image above is what we're trying to achieve here.
To determine the effects of various factors on health outcomes, we currently apply pharmacokinetic modeling over various onset delay and duration of action hyper-parameters. We combine that with some other parameters for each of Hill's criteria for causality.
The distributions in this type of data aren't super normal, and you've got the onset delays and durations of action, so regular Pearson correlations don't work so well. So, we mainly focus on change from baseline. There's a ton of room for improvement by controlling using instrumental variables or convolutional recursive neural networks.
Hybrid Predictive Control Black Box Models seem most appropriate.
Test and Training Data
The best file is probably arthritis-factor-measurements-matrix-zeros-unixtime.csv. It's a matrix of years of self-reported
Arthritis Severity Rating measurements and hundreds of potential factors over time.
Format
The first row is the variable names. The first column is Unix timestamp (seconds since 1970-01-01 00:00:00 UTC).
Pre-Processing
To make it easier to analyze some preprocessing has been done. This includes zero-filling where appropriate. Also,
the factor measurement values are aggregated values preceding the Arthritis measurements based on the onset
delay and duration of action.
Hyper-Parameters
The aggregation method and other hyper-parameters can be found by putting the Variable Name in either
- the API Explorer or
- in the URL
https://api.fdai.earth/VARIABLE_NAME_HERE.