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Comment by srvmshr | Hacker News Reader

That's one of the biggest roadblocks: hospital & healthcare entities will not not have permissible mass sharing of data. When switching hospitals, its really hard to get a full copy of the record for single person. Try that for whole population. I may be wrong, but hospitals also could have incentives to tightly hold on to their proprietary data - where they spent a large sum of money. I worked in ML+Dermatology & getting data was a lot of regulatory check.

Also, DICOM is the format for maximum interoperability from different radiology modalities & manufacturers. But annotation & markup methods vastly vary between institutions. There is no commonality or agreed upon standards on information interchange.

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Federated learning (with implementations such as FEDn [1]) is supposed to solve this problem; Only sharing training weights, but not sharing data.

Sure requires some coordination, but the legal parts should at least be solvable in this way.

[1] https://github.com/scaleoutsystems/fedn

I've been involved in a federated learning radiology project, the biggest issue is image technique and labelling.

Different centers practice very differently, with different imaging protocols, disease prevalences, and labelling/reporting.

This project was looking at renal masses and the only part that worked well with federated learning was image segmentation and probability that the mass is a cancer, this is a competency I expect out of a first or second year trainee.

It was horrible for predicting subtype of cancer as we couldn't get a good training set (few of these lesions are biopsied, specific MRI sequences that may help are not done the same way in every center) which is what the goal was and more of an experienced generalist/subspecialty radiologist skill.

Practically subtype doesn't make too much of a difference for the patient as if it's "probably a cancer" it'll just get cut out anyway, but highlights a challenge with federated learning.

BERT-like encoder models actually do help here, although unfortunately the dense representation is not as nice and clean as a class, it will at least map to something similar. If the hospital provides even a completely bizarre entity id, the requirement would be to provide the dictionary of descriptions and train the BERT-like model to associate the entity ids and descriptions.

> BERT-like encoder models actually do help here, although unfortunately the dense representation is not as nice and clean as a class, it will at least map to something similar.

Completely agree with this.

> If the hospital provides even a completely bizarre entity id

This is where it beings to fall apart, the hospital doesn't always provide an entity. In this specific project, some will argue "it's a big enough renal mass just cut it out, based on current evidence it's safest to remove" which while true at the moment doesn't help us in developing a model to prospectively predict the subtypes that do/do not need surgical excision.

Consequently, there is no biopsy and specific MRI sequences that are proving helpful at subtyping are missing, irrelevant for clinical care per current standard practice but hugely relevant if you're trying to change that practice as we need labelled data but we don't have the resources to have a radiologist/pathologist go over every case again and try to fill in some blanks en masse.

Im not sure it even has to be complicated and full on machine learning.

Could also just make it easy to share diagnosis to the a database stripped from personal information about the client, properly index it, share it worldwide, allow doctors to contact eachother automatically who match.

If it is a rare condition, reidentifying people from even small amounts of relevant data (age, gender, hospital visited) data is trivial. If it is a common condition, usually there is plenty of data already there.

Is that really possible? I don't understand the field exactly but at least if you use deep learning, you'll be able to recover inputs from gradients, and if you want to program it such that no other party is able to recover your inputs, that's called cryptography and homomorphic encryption is FAR from practical yet (like a million times slower than practical). Without rigid mathematical foundations, I would doubt that it's just a fancy way of gathering all the training data and doing the training and making it perceive as if everyone retained their own data.

I think it will happen much faster in China, India or maybe Japan than in the U.S for that exact reason. It's quite possible the U.S will lag a lot in healthcare soon.

Yeah you have to make it non-personal, as well as deal with the commercial side of it. I've seen a few initiatives, but they all seem to lack the pockets, drive and ambition of an arrogant Silicon startup. Even though it's annoying at times, it might be what can do the trick in this case.

> That's one of the biggest roadblocks: hospital & healthcare entities will not not have permissible mass sharing of data.

Maybe in some other country?

I am not certain how it works in all of Europe, but it doesn't seem straightforward in Sweden or UK.

In US and Japan, they will rather do the full roster of tests than transfer in radiology results from another hospital. Its not something to do with costs, but physician opinions. Every specialist has his personalized view of the patient in hand - so the corpus of information he needs is different.

As far as I know, in Sweden data about a patient is shared between every hospital... you need to sign up on a web portal, of course, but from then on, you can authorize a doctor or nurse anywhere to get your data with a click... you can go to any clinic and they have access to your full history. It's impossible to do anything properly if you don't have that. I just don't understand why in the US, it's acceptable for hospitals to be silo-ed like that to the detriment of everyone's health.

Here's a news report from 2017, when the system was being set up: https://www.bmj.com/content/357/bmj.j2069

Today, this has been working for years.

The US has multiple individual medical systems larger than Sweden with essentially the same abilities. Sweden has roughly the same number of hospitals as South Carolina.

What makes you think the Swedish system wouldn't scale to countries with 100's of millions of people??

Serious question - could it? How would that be accomplished?

What's nice is considering a global audience.

this may be a sufficiently powerful macro force to disrupt that

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