Ask HN: What medical datasets do you need?
We recently announced YC AI (https://blog.ycombinator.com/yc-ai/). This is only the first step. Our long term goal is to democratize AI development. We want to make it easier for startups to compete with the big companies.
One thing large companies have is data. We're experimenting with ways to allow startups to get similar assets, and we're starting with medical data.
If you're working on AI and need medical data, please help us by filling out this form: https://goo.gl/Dr9FzB.
138 comments
[ 2.7 ms ] story [ 225 ms ] threadI would love to know the prices patients out of pocket pay.
I have had doctors put me on addictive drugs, and then required me to show up for prearranged visits. I had one raise prices when I even asked about the price of the office visit. I learned to just show up, and pay the dude.
Yea--lost that respect years ago. At one time, I really respected the profession.
(I built this tool, so please be nice ;)
http://costatlas.iha.org/map?m0=TCCCOMP&p0=HMOPOS&m1=TCCCOMP...
The dataset spanning all of them is likely to be in the tens or hundreds of TB range, if not PB.
Anyway, it's completely legal. You just have to scrub the data pretty thoroughly before you sell it.
Now, I don't understand DP well enough and information theory/signal processing still seems a bit like "dragons be here" to me. But, I want to take a stab at trying to reason why he said that.
For example, take randomized response (the only DP technique I understand). That is vulnerable to a longitudinal attack: a person can query repeatedly to wash out the randomness. If you think about it, isn't it the almost the inverse of a repetition code (error correction)? There, you're trying to use redundancy (repetition) to remove noise.
If your signal processing professor was already taking that into account then I would be curious to know how that attack would work.
Sources like MIMIC are certainly interesting and valuable but it'd be great to get data longitudinal records, spanning years of coverage.
https://mimic.physionet.org/
https://www.physionet.org/search-results.shtml?q=ECG&sa=Sear...
Patient history would help too. (I know there's HIPPA to comply with, but as much as we can get can help train better classifiers.)
Also, anything from here: http://www.nature.com/neuro/journal/v17/n11/fig_tab/nn.3818_...
And the following: http://www.ukbiobank.ac.uk/imaging-data/ http://nmr.mgh.harvard.edu/lab/harvardagingbrain http://www.einstein.yu.edu/departments/neurology/clinical-re...
https://github.com/NeuroTechX/awesome-bci#brain-databases
We will use NLP and AI to provide structured data from unstructured medical data (encounter notes, etc...) stored in the EHR for both analysis and integration. For example, one of our partners right now wants to integrate directly into our EHR in order to run computer vision algorithms on top of uploaded eye exam images in order to help diagnose eye diseases. We give them access to the eye image and other patient data, including the encounter, diagnoses, etc. After they have trained their algorithms, we then allow them to hook directly into the encounter workflow to send alerts live to the doctors during the appointment. We want to be a platform to help other startups and researchers connect with medical data both for analysis and also to help make a meaningful impact directly to doctors' workflows and patient care.
We would love to help out and/or learn about any use-cases that others might have requiring medical data. If you would like medical data or would want to integrate directly into a doctors' workflow in their EHRs based on NLP/AI hooks, we would love to hear from you. You can reach out to me directly at ginn@stanford.edu
Then those with Diseases or conditions.
In my opinion, large datasets testing wide spectrums of hormones in a large population, tagged with any diagnosed endocrinological condition would be extremely valuable. I bet with this information, we could learn a lot without conducting actual physical studies, by simply sectioning the data appropriately.
I'm not a doctor though, so I don't know exactly what would need to be recorded, but having dealt with bizarre endocrine disorders that doctors don't really have any answers to, my gut feeling is that such a data set would be incredibly useful.
I foresee in less than ten years we will have a doctor in our pockets. No, it won't cure us and it won't replace a doctor, but it will give us all the information we need to have a 99% certainty of our condition.
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Second batch for animals and their conditions.
Third batch, agriculture. Take a pic of a plant and tell me all the info, fertilizers, cultivation, etc, bonus for pest id and treatment.
Pocket computers should be able to diagnose every living creature.
IBM's Watson at MD Anderson Cancer center did not work out real well for them. In other words, using AI in the realm of medical diagnostics is very difficult.
Say a user decides to self-diagnose and damages themselves (either through inaction or self-medication or whatever). What difference does it make whether they diagnosed themselves by browsing symptoms on wikipedia or using an advanced diagnosis AI on their phone?
Overall, of course, you're right. Liability is the problem with my suggestion. Doctors prescribe to treat, they also prescribe to meet the legally mandated standard of care and minimize second-guessing later. Looking at each patient as a unique snowflake-- or at least, part of a thinner-sliced group-- helps with the first, but directly undercuts the second goal. Such an approach would probably need to originate outside the U.S.
Extracting data from the EMR is very difficult because all EMR was originally intended to only be a storage place for data - not designed to output data back to a user.
Anecdotal, but I had a suspicious mole looked at, the doctor couldn't decide, got a second opinion from their colleague, he still was only 90% sure. And that's a relatively simple example. Doctors are some of the smartest/hard working people in society, and if they can still make mistakes, medical-grade AI is a long way off.
The benefit of medical-grade "AI" (in particular, multi-layered convolution neural networks) is that, given an aggregate of information (say, if you equipped every derm and oncologist with high resolution cameras and a set of parameters to standardize each datum), a trained professional[1] would be able to use that corpus as a very useful resource (used, obviously, in conjunction with their formal training and years of medical experience).
That being said - this is a question you should really be asking those who practice medicine or are actively in research for a living. Go pick up the last years issues of Nature Methods to see what problems they're encountering, and which technological gaps[2] (if any) they may have where YC AI might be applicable.
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[1] Needless to say, this is something I'd be very reluctant to release into the populace's hands, lest you have someone whip together some node.js backend and React iOS app :: "Do I have Skin Cancer? $10 to find out!". One shudders to think what sort of hysteria might follow.
[2] You would be surprised at how technologically adept some of the members of those research teams are -- within a month of DeepMind making the headlines on our tech blogosphere, I was seeing RNN being applied to their industry.
I've had this discussion with my SO (who is a doctor) many times, she strongly believes the breadth+depth of knowledge required for such an AI would be too great, but perhaps as a tool for GPs, or for specific, easy ailments (i.e. telling a patient what they don't have, and if it's serious enough to raise the issue to an actual doctor).
That seems way high. Correct diagnosis by doctors isn't 99%, or even close.
We can learn a lot from animals, and clearly the data should not be protected as much as for humans. Therefore, I'm wondering, where is all the animal data? Could we already start to build models using that data? This could be useful for researching conditions that are more difficult to get data for in the case of humans.
Why do you want to have the diagnosis before seeing a physician? (I'm legitimately interested in the answer.)
In my experience, it's about 50-50 with helping versus hurting making the correct diagnosis and providing accurate treatment.
Even in the US where you actually have to pay for medical stuff, it can't be more than $10 or so for a quick consultation?
You also have no idea of the cost of various diagnostic actions they might take. I once had blood drawn and got charged $600 for it because the lab they chose was out of network. I didn't even know there was a cost, let alone which labs to request.
Or you could see it this way :
$150 to have a nurse put a popsicle stick on your tongue and tell you that you are very lucky to have presented today because you have a suspicious growth - which on examination turns out to be a rare form of cancer that is easily cured if detected early, but most certainly fatal otherwise
It's times like this I realize just how different the rest of the world is.
Datasets are limited and expansion with AI would be huge.
One specific application - determining cost effectiveness of placing tourniquets in public places - much like the idea of having defibrillators at the mall. And funding community training, see the "Stop the Bleed" campaign.
[1]: http://graphics.wsj.com/medicare-billing/
[2]: https://www.wsj.com/articles/wsj-new-york-times-win-pulitzer...
I understand pharma not sharing but much of the hospital/broad/university data is produced with (at least some) public money.
Given you have surgeon [x] what are odds of a successful surgery with [x]. THIS is the guarded secret -- yet the most valuable.
If you have medical data (or want to be a cofounder) please email me :ransom1538 at gmail.com -- a prototype: https://www.opendoctor.io to find out data to this very question.
There is an interesting debate about whether this is a good thing or not. One argument is that it improves transparency and allows patients to make a better, more informed choice of who operates on them.
The counterargument is that most patients don't understand that there is an element of probability distribution involved. Perhaps more importantly, if the thought process of a surgeon changes from "performing surgeries to the best of my abilities and knowing I will lose my job if I am dangerous" to "all my results are published for public scrutiny so I need to have a survival rate as close to 100% as possible as that is all the public comprehend" may lead to surgeons only being willing to take on cases which are very likely to be successful, as taking a difficult or last-chance case will have a high probability of mortality and therefore will affect their numbers. This would be a loss for many people. I don't know if any research has been done to determine whether that has borne out or not though.
[1] https://www.nhs.uk/service-search/Consultants/performanceind...
What is a successful surgery? One with the least complications? No complications? Shortest recovery time? Best return to function? Best outcome for that particular patient?
It's not an easy question to answer.
CFS is interesting because:
a) Patients' symptoms appear to fluctuate "randomly" but are actually typically a complex function of genetics, blood markers, exercise, diet, medication and other factors.
b) There is considerable low-hanging fruit for pattern recognition, since despite the prevalence of the disease almost nobody has done serious ML work in this space.
c) Huge market opportunity - prevalence is comparable to HIV, and specialists often cite CFS as causing more disability [4] [5].
[1] http://simmaronresearch.com/
[2] http://www.nova.edu/nim/research/mecfs-genes.html
[3] https://med.stanford.edu/chronicfatiguesyndrome.html
[4] https://consults.blogs.nytimes.com/2009/10/15/readers-ask-a-...
[5] Dr. Daniel Peterson (Introduction to Research and Clinical Conference, Fort Lauderdale, Florida, October 1994; published in JCFS 1995:1:3-4:123-125)
People with these lifelong illnesses typically experience a roller-coaster of recurring symptom flare-ups, wreaking havoc with their lives. Yet there are patterns to the flare-ups. This is an opportunity to make a big difference for millions of people [2].
[1] https://www.aarda.org/disease-list/
[2] https://www.aarda.org/autoimmune-information/autoimmune-stat...
A major challenge is to get a large number of patients to continuously track their symptoms. Most want to know what’s in it for them. It takes substantial incentives for people to regularly report outcomes and use wearables for data collection. Until we can make the marginal cost hit zero, they need to benefit from their efforts and investment, preferably instantly.