I would use fitbits with daily step count, sleep duration and heart rate variability and breaths per minute and resting heartrate and body temperature to monitor patient progress.

I can ask the questions that would partition the useful dopplegangers. If you ask great questions you get the great answers. Then you just need to group the data intelligently.

What I can’t do is be a machine learning expert and database/sys admin and front end developer and negotiator with a public health system.

I’d let the patients vote on their next steps initially. I’d also have psych questionaires to measure their personalities. That would form part of the sympton /genetics doppleganger groupings. As would genetic profiles.

For CFS there aren’t that many logical next best steps. Most things have been tried, there is a sensible order to try things.

This is the case with most chronic illnesses.

However subsets within cfs are better off trying certain treatments before others.

The better a subset responds to a next treatment then the tighter the coupling of their doppleganger to that treatment. Then when any new people match that doppleganger you can reccomend they try that treatment. You’d shift there next best treatment orders around.

As you get more and more data the treatment recommendations get more and more granular and effective.

I could hire a naturopathic md to assign the treatment steps initially.

If anyone wants to do a start up - reach out, I can find funding. Just need a team. I’m going to do it.

I appreciate you sharing your knowledge. But I think you’re assessment of the situation is wrong. I think the general consensus is wrong and I have enough energy now to fafo. Given what I feel I know it feels immoral not to.