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 regression, gradient boosting, and a shallow neural net) on readily-available signals from the medical record (e.g., prior diagnoses), you can increase the c-statistic from 0.728 to 0.764. These machine learning techniques are well-suited to the data set and the empirical evaluation is strong, so this work should really stand on its own without trying to brand it AI.
There is some very high quality work on artificial intelligence in medicine being done today. Google Brain published a validation study of a convolutional net to diagnose diabetic retinopathy in December, and Stanford published similar work but applied to skin cancer. NIPS 2016 had an excellent Machine Learning for Health workshop with several works-in-progress:
http://www.nipsml4hc.ws/
What's the difference between AI and 'standard machine learning algorithms'? Seems like they are used interchangeably today. Especially if you count neural nets as a standard algorithm too - which, let's be frank - it is in today's world.
Machine Learning is just a tool belt. AI is the idea of intelligent machines being able to solve problems (and recognizing them) on their own. Therefore of course AI can utilize the tool belt that is Machine Learning.
What a silly headline. Here is a brief description of what was done, from the original article (helpfully linked by `henrypray):
> Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years
So, routine learning algorithms were applied to a task and performed better than the simplified (for simple computation) results of a prior learning algorithm.
Based on my experience with several doctors and other medical professionals here in the US, it appears as if they always diagnose problems with a simplified test or some kind of decision tree like the standard test they did here (the "established algorithm"). Doctors I have seen only seem to get the diagnosis right if it is a problem most people have.
The most blatent example, though not the only one, is when I spent months and thousands of dollars (with the help of my insurance company) chasing a problem in my SI joint. Four different physical therapists kept telling me I had referred pain from my spine. This is based on the fact that I felt the pain if I raised my leg when I was lying down. That seems like a pretty poor test. Any other doctor or physical therapist that actually looked at my SI joint said I had an inflammed SI joint problem. The problem turned out to be my hip muscles would get tight when I slept on my side.
I would guess the main problem is not the doctors themselves but the administration that doesn't trust the doctors to use their brains. If that is true, it is not silly that the AI result beat the simple test. Hopefully either they will start using better tests of trust doctors a little more to think on their own.
How do I interpret Missing BMI as a risk factor. In the ML: Neural Networks run BMI Missing was considered a top 10 risk factor. The body text said this about BMI Missing: "This study suggests that missing values, in particular, for routine biometric variables such as BMI, are independent predictors of CVD."
I'm having a hard time wrapping my brain around this concept.
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 cholesterol can be treated and thereby prevent heart attacks and other cardiovascular events.
In our ICU data we found that missing fields also predicted worse outcomes. My theory is that in sicker patients nurses are less worried about collecting all the data. But who knows.
Readit News
What the authors showed is that by training standard machine learning algorithms (random forests, logistic regression, gradient boosting, and a shallow neural net) on readily-available signals from the medical record (e.g., prior diagnoses), you can increase the c-statistic from 0.728 to 0.764. These machine learning techniques are well-suited to the data set and the empirical evaluation is strong, so this work should really stand on its own without trying to brand it AI.
There is some very high quality work on artificial intelligence in medicine being done today. Google Brain published a validation study of a convolutional net to diagnose diabetic retinopathy in December, and Stanford published similar work but applied to skin cancer. NIPS 2016 had an excellent Machine Learning for Health workshop with several works-in-progress: http://www.nipsml4hc.ws/
Deleted Comment
> Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years
So, routine learning algorithms were applied to a task and performed better than the simplified (for simple computation) results of a prior learning algorithm.
The most blatent example, though not the only one, is when I spent months and thousands of dollars (with the help of my insurance company) chasing a problem in my SI joint. Four different physical therapists kept telling me I had referred pain from my spine. This is based on the fact that I felt the pain if I raised my leg when I was lying down. That seems like a pretty poor test. Any other doctor or physical therapist that actually looked at my SI joint said I had an inflammed SI joint problem. The problem turned out to be my hip muscles would get tight when I slept on my side.
I would guess the main problem is not the doctors themselves but the administration that doesn't trust the doctors to use their brains. If that is true, it is not silly that the AI result beat the simple test. Hopefully either they will start using better tests of trust doctors a little more to think on their own.
I'm having a hard time wrapping my brain around this concept.
Thanks
The people who don't go to the doctor because they feel fine aren't included in the study.