[brandonb]

Startups often have health data and an interest in machine learning. For example, we presented research at a NIPS workshop where we trained an LSTM to predict abnormal heart rhythms from about 793 million heart rate measurements. The heart rate measurements came from our Apple Watch app, Cardiogram, and the "gold standard" data came from a study we're running with UC San Francisco (wsj.com/articles/new-study-seeks-to-use-deep-learning-to-detect-heart-disease-1458240739).

It's not just us. The NIPS machine learning for healthcare workshop had hundreds of attendees this year from both industry and academia: https://www.nipsml4hc.ws

If you're an ML researcher or engineer and want to use machine learning to save lives, feel free to email me. I'm [e-mail]. Happy to talk about our company or point you to relevant research.

[spdustin]

I'd be more interested, as someone who has occasional atrial fibrillation, if my data results in any particular predictions for me as to severity, likely occurrence while I'm sleeping, etc. I'm 100% on board with my data benefitting all of humanity, but as a sufferer, how can it more immediately provide me with any actionable insights.

[brandonb]

We do plan to expose predictions for you within Cardiogram. The biggest barrier is that any predictions relating to a health condition (like atrial fibrillation) are FDA-regulated, so there's a high level of scientific validation that must happen first.

[kmlng]

Sounds like you have paroxysmal AF. I can't tell you much about any direct benefits ML could provide there.

I do work on data from persistent AF patients. Specifically, trying to predict AF recurrence after treatment with electrical cardioversion. Basically, electrical cardioversion is an effective treatment for some subset of persistent patients, but for another subset it is not. Doctors have a hard time deciding which patients can benefit from electrical cardioversion and which will not. If we can build a model that predicts this, we can avoid unnecessary procedures (which always carry some risk) and explore other treatment options instead. If this works well, it would directly benefit individuals.

[spdustin]

One idea is that I may be able to correlate it with other data I log into Health, like caffeine input, stress levels, sleep patterns, etc.