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I worked on one of the first wearable foundation models in 2018. The innovation of this 2025 paper from Apple is moving up to a higher level of abstraction: ins
by brandonb 1y ago
I worked on one of the first wearable foundation models in 2018. The innovation of this 2025 paper from Apple is moving up to a higher level of abstraction: instead of training on raw sensor data (PPG, accelerometer), it trains on a timeseries of behavioral biomarkers derived from that data (e.g., HRV, resting heart rate, and so on.).
They find high accuracy in detecting many conditions: diabetes (83%), heart failure (90%), sleep apnea (85%), etc.
- puppymaster 1y agoreminds me of Jim Simons of Renaissance advise when it comes to data science - sort first, then regress.
- deleted 1y ago[deleted]
- dinobones 1y agoNot sort in the literal way right? https://stats.stackexchange.com/questions/185507/what-happens-if-the-explanatory-and-response-variables-are-sorted-independently https://stats.stackexchange.com/questions/185507/what-happen...
- clickety_clack 1y agoThe guy was sorting the X separately from y? That can’t be a real.
- falcor84 1y ago"Nothing is foolproof to a sufficiently talented fool"
- tomrod 1y agoNot every day you find pseudo permutation in the wild!
- throwaway314155 1y agoHad the phrase "foundation model" become a term of art yet?
- brandonb 1y agoBy 2018, the concept was definitely in the air since you had GPT-1 (2018) and BERT (2018). You could argue even Word2Vec (2013) had the core concept of pre-training on an unsupervised or self-supervised objective leading to performance on a downstream semantic task. However, the phrase "foundation model" wasn't coined until 2021, to my knowledge.
- throwaway314155 1y agoI guess I just find the whole "foundation model" phrasing to be designed in a way to pat the backs of the "winners" who would of course be those with the most money. I'm sure there are foundation models from groups that aren't e.g. OpenAI, but the origins felt egotistical and asserting that you made one prior to the phrase's inception only feels more-so. Had you merely called it an early instance of pretraining, I'd be fine with it.
- crorella 1y agoInsurance and health insurance companies must be super interested in this research and its applications.
- jeron 1y agoI'm sure they're also interested in the data. Imagine raising premiums based on conditions they detect from your wearables. That's why it's of utmost importance to secure biometrics data
- brandonb 1y agoAt least in the US, health insurers can’t raise rates or deny coverage based on pre-existing conditions. That was a major part of the Affordable Care Act.
- abenga 1y agoThe ACA will not survive the next couple of years.
- daveguy 1y agoThat's what they said in 2016.
- abenga 1y agoThis time he has control over all the arms of government + the support of much of the private sector though. The last time there was at least some push-back.
- daveguy 1y agoThey had the exact same thing last time too. Control over all arms + support of much of the private sector. We told them to go fuck themselves then and we will again.
- teiferer 1y agoWhat is an "accuracy" of 83%? Do 83% of predicted diabetes cases actually have diabetes? Or did 83% of those who have diabetes get diagnosed as such? It's about precision vs. recall. You can improve one by sacrificing the other. Boiling it down to one number is hard.
- topaz0 1y agoThey use the area under the receiver operating curve, which is a pretty standard way to boil that down to one number.
- teiferer 1y agoAh, thanks for the pointer! https://en.m.wikipedia.org/wiki/Receiver_operating_characteristic https://en.m.wikipedia.org/wiki/Receiver_operating_character... So, 83% is actually not that great, given that you can achieve 50% by guessing randomly.