3 ms·

I see a lot of machine learning work on medical imagery, which is great, but it seems like this is solving a problem that the human brain is already pretty good

I see a lot of machine learning work on medical imagery, which is great, but it seems like this is solving a problem that the human brain is already pretty good at (image recognition). I wish I saw more work being done on finding patterns in medical data in numeric formats which the human brain is terrible at. Is there much of that going on?

toomuchtodo 7y ago
The human brain could always use a bit of help. Think of these sorts of tools as medical IDEs for practitioners. Anything that reduces costs, improves outcomes, or both, are welcome.
jcims 7y ago
You're absolutely correct but from what I can see there's going to need to be a lot more work done in collecting and standardizing that data before it's available in sufficient quantity and quality to do anything with. I think a more aggressive approach to normalizing externalities (primarily regulated diet/nutrition) would help as well. There's another completely (to me) unintuitive angle as well. Andrew Lo and some folks from MIT Sloan have published a paper about using clinical trial data to predict which medicines will be approved by the FDA in order to help reduce investment risk and unlock dollars. He does a pretty good talk about it here - https://www.youtube.com/watch?v=AzELyaVf0v8 https://www.youtube.com/watch?v=AzELyaVf0v8 He's on a recent episode of Linear Digressions discussing this as well. http://lineardigressions.com/episodes/2019/12/8/using-data-science-to-make-hard-prioritization-decisions-behind-the-scenes-of-an-analysis-to-predict-drug-approvals http://lineardigressions.com/episodes/2019/12/8/using-data-s...
deleted 7y ago
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anthony_doan 7y ago
> Is there much of that going on? Most of that are statistical methods in statistic and not ML. We're talking about survival analysis, longitudinal analysis, clinical trial, nonparametric statistic, etc... From my experiences ML is too dependent on large dataset. Medical data are often high dimensional and small. My thesis papers have leverage two statistician works to make decision trees and ensemble leverage more statistic to handle medical data (high dimensional data). As noted by Dr. Harrell, statistician, ML is much more suited with less noisy data, medical image. Also inference is most more important in the medical field that just prediction.

toomuchtodo 7y ago

The human brain could always use a bit of help. Think of these sorts of tools as medical IDEs for practitioners. Anything that reduces costs, improves outcomes, or both, are welcome.

jcims 7y ago

You're absolutely correct but from what I can see there's going to need to be a lot more work done in collecting and standardizing that data before it's available in sufficient quantity and quality to do anything with. I think a more aggressive approach to normalizing externalities (primarily regulated diet/nutrition) would help as well. There's another completely (to me) unintuitive angle as well. Andrew Lo and some folks from MIT Sloan have published a paper about using clinical trial data to predict which medicines will be approved by the FDA in order to help reduce investment risk and unlock dollars. He does a pretty good talk about it here - https://www.youtube.com/watch?v=AzELyaVf0v8 https://www.youtube.com/watch?v=AzELyaVf0v8 He's on a recent episode of Linear Digressions discussing this as well. http://lineardigressions.com/episodes/2019/12/8/using-data-science-to-make-hard-prioritization-decisions-behind-the-scenes-of-an-analysis-to-predict-drug-approvals http://lineardigressions.com/episodes/2019/12/8/using-data-s...

deleted 7y ago

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anthony_doan 7y ago

> Is there much of that going on? Most of that are statistical methods in statistic and not ML. We're talking about survival analysis, longitudinal analysis, clinical trial, nonparametric statistic, etc... From my experiences ML is too dependent on large dataset. Medical data are often high dimensional and small. My thesis papers have leverage two statistician works to make decision trees and ensemble leverage more statistic to handle medical data (high dimensional data). As noted by Dr. Harrell, statistician, ML is much more suited with less noisy data, medical image. Also inference is most more important in the medical field that just prediction.