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by nbashaw 2193 days ago
Totally agree! It's going to take a whole new sub-specialty of ML to make any sense of it. But I think there's a positive feedback loop where more data → better algorithms → more data, because more people opt into the system in order to get access to the algorithms.
2 comments

I think there might be an even bigger problem in coming up with any kind of response that we didn't already know. E.g. the advice "Don't smoke, stop eating before you're full, eat more vegetables, eat less bread and pasta, drink less alcohol, prefer water instead of drinks like juice, milk or soda, stop eating processed sugar, avoid saturated fats, get regular cardio, avoid stress, get enough sleep and do so at roughly the same time every night" is likely to be correct for the vast majority of people. Having top-of-the-line ML crunch a bunch of data only to spit out the exact same recommendation to everyone doesn't really accomplish much. Maybe more specific recommendations would be possible in a decade or two if we start having detailed data to look at, but considering how hard a time we've had just answering simple questions like 'are eggs good or bad for you?' in general, I'm not really all that optimistic about our ability to make precise individual recommendations that are not already obvious.
Generally I think you're right. But also I think there are a ton of edge cases we could discover based on complex interactions of genetics, environmental factors, disease history, etc.

Also there's a lot more urgency when an algorithm detects an anomaly and asks you to schedule a blood test!

Do you really need ML to tell you to eat better and exercise?
No but you might to pull together a bunch of noisy data points until a confident prediction of early stage serious disease.
This happens relatively frequently actually.

Guy buys an Apple Watch and gets notified of an arrhythmia, sees his doctor and he averts a potentially serious incident: https://www.medicaldevice-network.com/news/apple-watch-atria...