back
user profile
brandonb
9,346karma·1,335submissions·January 17, 2011
about
Data for good.
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
recent activity (1,335 total)
comment
This could be a signal that people who don't visit the doctor often are more likely to develop CVD. It makes sense—if caught early, risk factors like high BMI, high blood pressure, and high LDL c…
comment
As others pointed out, it's an overreach to call this artificial intelligence. What the authors showed is that by training standard machine learning algorithms (random forests, logistic regressio…
comment
Another idea is one-shot learning using deep generative models. DeepMind had a paper on this last year:
https://arxiv.org/abs/1603.05106 …
comment
Here are a few efforts from the Y Combinator and broader tech communities that help with those goals. On veterans, several YC alumni, after working on the healthcare.gov rescue team, went on to help s…
comment
True -- the available data sets (like MIT-BIH arrhythmia database) are quite small. In our case, we launched a study with UCSF Cardiology which has recruited about 10,000 people so far, and that'…
comment
The app is free to you, but if we can detect (say) diabetes early and route you to the right medical care, we save the healthcare system money. We've built a platform called Cardiogram Connect wh…
comment
Not for now unfortunately--but maybe once we get a little larger.
comment
If you're a strong software engineer but without any particular expertise in deep learning, feel free to email me as well. brandon@cardiogr.am :) We (Cardiogram) in particular are hiring for: mob…
comment
I wrote a bit about the systematic issues preventing promising AI research from moving to production in medicine, including a very brief history of MYCIN:
https://blog.cardiogr.am/thre…
comment
This is the second major study applying deep learning to medicine, after Google Brain's paper in JAMA in December, and there are several more in the pipeline. If you've developed expertise i…
comment
Sure, my email is brandon@cardiogr.am.
comment
The NIPS workshop was Machine Learning for Health. The paper isn't out as a PDF since a more mature version of the work will be submitted to a medical journal:
http://nipsml4hc.ws …
comment
Not sure if this is what you're looking for, but Andrew Ng touched on a lot of pragmatic parts of software engineering in machine learning teams at Deep Learning School:
https://www.yo…
comment
The math is more approachable than it may seem. My recommendation is to read textbooks (not online tutorials or crash courses), which will guide you through the mathematical concepts and notation. Eac…
comment
Yes! Deep learning is a young field. I have a B.S., for example, and have published work at Interspeech (speech recognition conference) and NIPS workshop (machine learning conference). You can too. My…
comment
I can't edit my post anymore, but as of right now, the link works over http rather than https:
http://www.nipsml4hc.ws/ …
comment
For those interested in AI in medicine, the most recent NIPS held a workshop on exactly that topic: https://nipsml4hc.ws There's quite a bit of active research applying LSTMs, CNNs, a…
comment
Just wanted to say: thank you for your service!
comment
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 h…
comment
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 mil…