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Looking forward to when someone makes a significantly sized, fully labeled medical dataset. Currently, the main thing holding us back on diagnostics using mach
by unityByFreedom 9y ago
Looking forward to when someone makes a significantly sized, fully labeled medical dataset. Currently, the main thing holding us back on diagnostics using machine learning isn't the software or hardware- it's the lack of labeled data, which is costly to prepare because it requires technologists to work alongside doctors. Still, the results could be huge.
I think machine learning through medicine will save more lives than self driving cars.
- PeterisP 9y agoIt's highly problematic due to privacy issues. It would be a nightmare to get a public release for the underlying unlabeled data, especially since we know how well deanonymization can work.
- unityByFreedom 9y agoThere already is some that is publicly available, like the Kaggle lung cancer competition's dataset. I'm not saying it's easy on any front. It is possible, and it would be hugely beneficial to humans to have such a dataset. Raising awareness about the problem may be one step towards solving it.
- nonbel 9y agoThe problem with a lot of medical labeling is the labels are not very consistent/accurate to begin with. Just go to three doctors with the same symptoms and see what they say...
- unityByFreedom 9y agoThat is surmountable. You can use annotator agreement, and, algorithms don't need perfectly labeled data in order to improve. Any effort to create additional medical datasets would probably help enhance machine learning diagnostics. The data that you want to be perfect is the test set, which is smaller than training sets.