For most of these metrics, zero is not a logically possible data point. For example, somebody with an HbA1c of 0 would be dead.
10,056 karma · joined January 17, 2011
Co-Founder at Empirical Health (https://empirical.health). Don't die of heart disease.
Before: Co-Founder @ Cardiogram (ML for heart health)
CTO at Sift Science (YC S11, machine learning to fight fraud)
Data Science @ UCSF Cardiology
HealthCare.gov rescue team
Google (Android speech recognition, search ads ML)
brandonb.cc
(There is such a thing as Medicare advantage, where a patient can choose to put their Medicare dollars toward private insurance, but it's not part of the initial launch of this program.)
The other uses are honestly pretty standard rhetorical patterns; they do not seem especially AI-flavored to me.
Historically, insurance has paid for activity: time spent in visits, RVUs generated, and minutes logged. This was a reasonable starting point, but the flaw is that there's no strong incentives to be efficient.
ACCESS is explicitly a "deflationary" approach. Medicare has set the payment rates high enough to be viable for startups, but low enough that you have to use software (including AI) to deliver a large part of your program.
So Medicare has basically created economic incentives to reward software without prescribing the exact shape of the programs. I thought it was a really interesting approach and builds on 15 years of lessons from CMMI (Medicare's innovation group).
This statement is from lipidologists (basically, people who study cholesterol), but the American Heart Association released similar guidelines in Mar 2026 which recommend Lp(a) testing for everybody.
Only 1 in 400 people test Lp(a) today, although that's up 22x in the last decade: https://www.empirical.health/blog/lpa-testing/
Four major drugs are in development that target Lp(a) specifically; one of them lowers it by 94%.
The patient population was 3600 people with high-risk diabetes, but not atherosclerosis. So they're at elevated risk compared to the average person.
PCSK9 inhibitors are still expensive (about $1,800 per year), with pretty limited insurance coverage. But this will likely change as the evidence builds.
It includes both mathematical formulas and PyTorch code.
I found it a bit more practical than the Sutton & Barto book, which is a classic but doesn't cover some of the more modern methods used in deep reinforcement learning.
These newest guidelines from the AHA/ACC, released yesterday, operationalize this partly by using a new set of equations that predict cardiovascular risk over a 30-year period (rather than 10 years).
> EMA-based training dynamics like JEPA’s don’t optimize any smooth mathematical function, yet they provably converge to useful, non-collapsed representations.
All the papers say EMA avoids “representation collapse” without justifying it. Didn’t realize there were any theoretical results here.
The specific biomarkers being predicted are the ones most relevant to heart health, like cholesterol or HbA1c. These tend to be more stable from hour to hour -- they may vary on a timescale of weeks as you modify your diet or take medications.
If you're looking to learn about JEPA, LeCun's vision document "A Path Towards Autonomous Machine Intelligence" is long but sketches out a very comprehensive vision of AI research: https://openreview.net/pdf?id=BZ5a1r-kVsf
Training JEPA models within reach, even for startups. For example, we're a 3-person startup who trained a health timeseries JEPA. There are JEPA models for computer vision and (even) for LLMs.
You don't need a $1B seed round to do interesting things here. We need more interesting, orthogonal ideas in AI. So I think it's good we're going to have a heavyweight lab in Europe alongside the US and China.
Not only does fiber reduce cardiovascular mortality by 26% (by cutting cholesterol), surprisingly enough, fiber even reduces your risk of cancer by 22%: https://www.empirical.health/blog/dietary-fiber-reduces-all-...
(Oatmeal is high in fiber, among other things, which I think is part of what's going on here.)
A YC company, Athelas, started by developing an at-home blood test analyzer (they've since pivoted).
The most surprising thing I learned that coconut milk is a lot less healthy than I would have guessed (high in lauric acid).
I had always heard that dark chocolate was heart healthy, in spite of being high in saturated fat. In addition to the effect of antioxidants, the stearic acid in dark chocolate quickly converted by your liver into oleic acid (a monounsaturated fat).
* ARPA-H ADVOCATE: autonomous AI cardiologist.
* CMS ACCESS: pays for outcomes, not visits, with reimbursement set that you have to use AI to scale.
* FDA TEMPO: lets AI devices deploy before full clearance
* Utah: AI can prescribe medication (refills at first).
I've been working on AI in medicine for 10 years. I'm honestly surprised at how fast the regulations are evolving.
> HDHP status was associated with worse OS (hazard ratio [HR], 1.46; 95% CI, 1.19-1.79) and CSS (HR, 1.34; 95% CI, 1.01-1.77) among cancer survivors.
"OS" is overall survival and "CSS" is cancer-specific survival.