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AI Can Predict Heart Attacks More Accurately Than Doctors (digitaltrends.com)
An anonymous reader quotes a report from Digital Trends: Scientists from the University of Nottingham in the United Kingdom have managed to develop an algorithm that outperforms medical doctors when it comes to predicting heart attacks. As it stands, around 20 million people fall victim to cardiovascular disease, which includes heart attacks, strokes, and blocked arteries. Today, doctors depend on guidelines similar to those of the American College of Cardiology/American Heart Association (ACC/AHA) in order to predict individuals' risks. These guidelines include factors like age, cholesterol level, and blood pressure. In employing computer science, Stephen Weng, an epidemiologist at the University of Nottingham, took the ACC/AHA guidelines and compared them to four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. The artificially intelligent algorithms began to train themselves using existing data to look for patterns and create their own "rules." Then, they began testing these guidelines against other records. And as it turns out, all four of these methods "performed significantly better than the ACC/AHA guidelines," Science reports. The most successful algorithm, the neural network, actually was correct 7.6 percent more often than the ACC/AHA method, and resulted in 1.6 percent fewer false positives. That means that in a sample size of around 83,000 patient records, 355 additional lives could have been saved.
the AI took the easy route and was scaring people into having heart attacks. ;)
Anons need not reply. Questions end with a question mark.
Will this be available on github?
Nope.
Will this be available on a free to use webpage?
Fucking no!
This will just be used be insurance companies to figure out who to shaft.
these methods "performed significantly better than the ACC/AHA guidelines,"
Yeah outperforming the general written guidelines is totally the same as outperforming a live doctor.
"Helpful tool that may substitute rule-of-thumb guidelines for doctors", maybe.
Because if it could we could use some here...
The trouble with data mining (my job) is you can overoptimise for microdetails that are not normal in the current data. I notice that they're using one group of test data for both the training and the tests, and that would cause over optimization.
" This cohort was then randomly split into a 75% sample of 295,267 patients to train the machine-learning algorithms and the remaining sample of 82,989 patients for validation"
So for example, both sets would cover the same 10 years over Brexit, wars, economic events, and diet fads, all of which affect heart attacks. If there are micro details related to that, then the algorithm actually works better FOR THAT SET OF DATA, it does not necessarily work better for the 10 year period beginning now, with a different set of events.
Yet in use the algorithm would be applied NOW to over the NEXT ten years.
So interesting, promising, but not as verified as they believe it is.
More accurate:
"Machine Learning Algorithms Can Predict Heart Attacks More Accurately Than Doctors, One Study Finds."
He thinks AL is sooo cool. So he want to go to college one day and learn how to use computer so he can blog about AL on fad news site Slashdot.
That's a bunch of malar~ '^ #~ g^g^g^g^g @aa` a , % [NO CARRIER]
Table-ized A.I.
If the reverse were true in 2017, that a doctor looking over a patient's demographics, questionaire answers and blood test numbers could outperform the AI.
That is, have an order that legally prohibits the use of AI/AI-derived treatments in nearly all cases. For the rest, a statement accurately documenting how no medical personnel were reassigned or terminated due to AI implementation - whether by direct or indirect means.
Twitter supports and protects racists - by smearing their critics with the "Hate Speech" label.
The biggest problem with these guidelines (the paper calls it an algorithm) is that humans optimize their predictive power using simple statistical analysis based on a training dataset. Then medical professionals are told the guidelines can be reasonably used anywhere. Throw it some data from a vastly different hospital and it will degrade in performance very quickly. For example, create rules using data from academic hospital, but test it on a rural hospital and the results won't be as good. There are different label distributions and often times even feature distributions.
If you use machine learning to train on the same hospital that you test, of course it will be better than a model created by humans on a different hospital.
I see this happen all the time with readmission prediction. There's a very popular system called LACE that was optimized for a specific hospital. It's simple to implement and test so there's a lot of reproduction. LACE has wild swings in performance (from AUC=0.55 to AUC=0.75 depending the dataset). Researchers will often compare LACE to a machine learning method like Random Forest, training and testing on same hospital, and shockingly the results are better.
So can the AI's decision making process be reverse engineered and fed back into the ACC/AHA guidelines to improve them?
In law enforcement you even get feedback loops where the AI is trained on the police arrest data, which guides the police to where to place enforcement for better arrests, which guides the AI which guides the police which guides the AI which guides the police....
Obviously is focusses the police in a crime area and all their arrests are focussed on that area because that is where they are.
The companies hire smart people, who understand the problem, but the corporate interest is to *sell* the AI, and this feedback loop also makes the AI look better.
There does appear to be an awful lot of sloppy science going on in datamining currently.
And one can "meta-train" for the test data group. Like
* Train
* Compare to test set
* Worse than guideline result => Change training parameters
* Train
* Compare to test set
* Still worse than guideline result => Change training parameters more
* Train
* Compare to test set
* Better than guideline result => Publish
I will be impressed, if it is better than a human doctor on new cases.
The AI does not outperform a doctor. Does not outperform any doctor period.
It has managed to perform better than guidelines.
Now let's consider that the AI is basing this on data gathered by medical science. What it's outperforming in actuality is thus old prediction models.
Overall, quite happy about it but let's not pretend that AI had 5 hours of sleep in the last 48, had a shower, drove to work, checked on the kids and yet manage to perform all work duties admirably. -because then it might actually outperform a doctor.
A 'singular oddity' is an event that cannot be explained and only happens when you are alone.
It will not be long before the AI neural network aggregate more data and determine, these 335 lives are not worth saving. What is the real incentive for artificially intelligent to save the naturally stupid?
sed -e 's/Chuck Norris/Rajnikant/g' joke > fact
AI's batting record
predicted 4,998 out of 7,404 positive cases 67% correct
predicted 53,458 out of 75,585 negative cases 70% correct
Definitely better than a coin flip, but not much.
So the interesting question would be what is it missing that is causing the errors?
Not just on /. It seems to me that these stories about AIs & automation/robots taking human jobs have been all over the place in the past few months. I consciously try to avoid most mainstream U.S. media, but I can't help some incidental exposure. I also listen to NPR ~ 2hrs/week and they've been doing a series of stories on this general topic as well.
It sure feels like a systematic effort at psychological conditioning. Is this simply the media trying to get back at Trump by blaming automation rather than immigration for displacing U.S. born workers? Is it just some current fad in the media which is going to pass when a majority of people get bored with it? Or perhaps it's just some distorted perception/selective attention on my part. It still feels sort of weird.
I find doctors to be quite bad at routing diagnostics, so I think a roll of a d100 has at least as good a chance of predicting heart attack as most doctors.
Doctors are generally good at minor surgery, prescribing drugs, and addressing simple injuries. Beyond that, diving meaning from chicken bones seems to be just as accurate as doctors in predicting and/or diagnosing general issues.
This speaks more to the growing incompetence of people in our medical profession than it does to advancements in algorithms. Have you ever spoken to a modern PA (good luck speaking with an actual doctor)? I could predict the time while they are staring at a clock with better accuracy. Medical schools aren't teaching what they once did, either. That profession is in a backward slide as much as any other in America. I also have no doubt that big pharma would LOOVE to replace their PAs with robots (that they own) that will just endlessly prescribe their meds. This is more complex than OMG AI, I'm afraid.
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.
At a population level I can see where being able to predict who will have a heart attack in the next 10 years would be helpful, but how much value is there for an individual?
Is the idea that a person begin preventative measures now and avoids the heart attack in the future? This algorithm wouldn't seem to be very helpful in planning surgical interventions like clearing blocked arteries.
...to the giant Slashdot circlejerk. There are people here who still take Ray Kurzweil seriously.
Is this a possibility? Predict the heart attack, and then issue a jolt to prevent it happening and get rhythm ?
by code. Or do you still fall for their game?
> The artificially intelligent algorithms began to train themselves using existing data to look for patterns and create their own "rules." Then, they began testing these guidelines against other records. And as it turns out, all four of these methods "performed significantly better than the ACC/AHA guidelines," Science reports.
This is merely back testing. It's easy to come up with an algorithm that works with data set A and they works with data set B. People have been claiming back tested algorithms to predict stock market returns and election returns for ages. And in forward testing the algorithms always fail.
This is not to say that a machine-based approach to predicting heart ailments cannot work. But for it to be proven, it has to be forward tested: it has to work with new patients.
Summary is not clear, neither is TFA. Does AI better use the usual data (age, cholesterol level, blood pressure), or did it use other data (waist size, alcohol intake, omega-3/omega 6 fat intake...)?
Hi @BeauHD . Is there any software or app to check fitness level? or calories calculator? if yes then kindly share the name and the source of the app.
thanks and regard
Mathew Haddin
NY. USA