How a mathematician constructed a decision tree to solve a medical problem
fastml.com
fastml.com
As the song says, "everything old is new again":
https://en.wikipedia.org/wiki/Knowledge_engineering
I'll eventually automate myself out of the office and then go sit on a beach somewhere.
I can't imagine there are more possible diagnoses than there are fictional characters and famous people.
BTW. try to make it guess itself ;).
The game report is interesting: http://imgur.com/5YRk8jv
The decision algorithm in the article is amazing in its accuracy and simplicity. There is probably some other low-hanging fruit in reproducing this method in other areas of medicine by working with someone who already has a high degree of accuracy in their diagnoses, but doesn't fully understand their own process. But outside of areas where anyone has the current ability to make such accurate diagnoses, it would be a lot more complicated.
You'd need to determine which factors are important, which of those are covariant, and how that relationship works. Then from there, work over tons of data to establish a bayesian model with weightings to apply to each factor so that you can observe the evidence, plug it into the model, and get out your probability distribution over possible diagnoses. It's no easy task, but I believe that this kind of approach is the future of medicine.
Shame about the patent - why would someone do that?
[1] http://en.wikipedia.org/wiki/Expert_system [2] https://probmods.org/ [3] https://probmods.org/conditioning.html#example-causal-infere...
But Norvig also put them in context, and left me with the impression that Expert Systems were proven inferior to Bayesian approaches for a lot of the problems they had been developed for.
I can well imagine, though, that there are problems where expert systems are a good fit.
As an interaction model and as a way of guiding control flow, as a weird and different way of handling user interaction and building extensible applications, I think the EMYCIN example from PAIP is pretty cool. I've wondered how possible it could be to use something like EMYCIN to write webapps ...
(Wikipedia makes it sound very complex, but you can do decision tree learning from hand from tabulated data easily, it is just tedious)
There is currently some confusion about decision trees used in decision analysis because the term "decision trees" is also used in machine learning but the approach is unrelated to decision analysis.
http://en.wikipedia.org/wiki/Glossary_of_patent_law_terms#Au...
It's usually to protect their idea, so they can use it in a product or business venture. There's some good information on patents and the way they are used on Wikipedia.
Anyway, the students loaded the system with rules from literature, interviews, etc and the testing started. Soon it was clear that there was mismatch between what system suggested and what doctors diagnosed. Not always but more than expected. The rules were updated and it was a bit better, but still not there. After some back and forth finally the doctors were told to speak aloud about what they are doing examining the patients - and it become clear that doctors used additional criteria, not mentioned in the interviews (when directly asked about that stuff). And even then it was not enough to explain the differences! Simply the doctors were using additional rules that they themeself were not fully aware they were using.
Sadly I don't know what happened to that project or any more details but the implications from this story always make me think... even highly trained individuals using very strict and well defined decision process end up with result that they can't fully explain! What if we could make this hidden expertize explicit to better train future doctors or just check if it is even valid?
And here we are almost thirty years later, and doctors in the US are still getting paid $250k or more to do stuff software could do, were it allowed, better, faster, and much cheaper.
My prediction is medicine will be one of the last fields to benefit from automation, simply because its greedy practitioners have a monopoly and won't give it up without a terrible fight. Sure, you'll increasingly see them rely on expert systems, but you won't be allowed to cutout the middlemen and go straight to Dr. Watson itself. They'll still be extracting their pound of flesh from us for many decades to come.
It depends on how sophisticated the machine is, I think. Doctors make mistakes all the time.
How is this any different than comparing Drug A to Drug B? You should choose the one that's more effective (and/or cheaper), why should emotion or the human-touch get into it?
Having said that, if you're paying the bill yourself, go nuts, but I'm in an environment where the public foots most of these bills, and therefore has some say in my opinion.
Where X is a big number, I think the self-correcting software will do better than any human. Not perfectly, but better, which is enough to make me choose the machine.
[1] http://www.sauropodstudio.com/dev-diary-number-fifteen-ai-ba...
Effectively, a lattice model is a decision tree about the different factors affecting an option's value, and thus the resulting value.
I see nothing in http://scholar.google.com/scholar?q=frenkel+Arutyunyan+kidne... and given the context, it was likely buried in an obscure Russian journal & so of no value to English readers anyway.
> This article is really crappy and doesn't explain what they did at all.
Seems like a good explanation to me.
> For example, why diddn't they mention probabilist networks, which dominates this field.
Because those weren't used much back then.
> And I doubt that 240 data points is enough to write a paper.
Sure it is. Why wouldn't n=240 work?