Machine Learning can be of some use in analyzing all this data. However with ML there are usually false positives, false negatives, and accuracy below 100%. I

Machine Learning can be of some use in analyzing all this data. However with ML there are usually false positives, false negatives, and accuracy below 100%. If the ML says you have a 61% chance of having had a heart problem, is that meaningful to anyone? How many false alarms or missed alarms can be tolerated? These things already happen with physicians, but diagnosis is a process, and they can keep evaluating over time. ML could also analyze long term data, but that means it could take a decade to gather and train it, and it might only be valid for the people it is trained on. I'm sure we'll figure some of this out eventually, but it takes RCT's and some cleverness to figure out how to turn data into information. Also people vary a lot, and it is increasingly apparent that one treatment or diagnosis method doesn't fit all people. It's a tough set of problems. That doesn't mean no one should work on it. I think the areas where the most progress will be made soon are likely to be those areas where there currently is no good diagnostic, or when the risks of a wrong answer are less harmful. You can try to make some sense of the data yourself. You know what you are doing at any one time, and can probably remember what you did during the current day. So you know when you were walking, sitting still, driving, etc. If there was a period of elevated heart rate and you were sitting still typing on a computer what else happened then that might affect your heart rate? Food you were eating? Stress from people around you? You can look for patterns where your HR changes when something in the environment changes. Physicians don't have a good idea of how environment affects people. They'll ask general questions like "is anything bothering you lately?" or "how are you doing at work?" but they don't see what each moment of your day was like. But you yourself know that. So perhaps HR data would be useful for looking for environmental influences on health. Until someone tries it, nobody knows.