So, essentially, this is a contest to make a way to predict who is most at risk for going back to the hospital.<p>while this sounds nice, there are some issues.<p>1. How can this do anything but hurt people? Medical professionals do all they can to keep people from returning to the hospital, explaining to patients what they should be doing in a medical sense, the only real use is to deny insurance or increase rates on "high risk" people.<p>2. Should they implement the winning solution, then act on it by sending additional "how to be healthy" propaganda or otherwise attempting to prevent those people, the pattern of behavior of will change accordingly, thus likely breaking the predictive capability.<p>This is not like the netflix "present better suggestions" problem. This does not need to be that fast, efficient, nor as creative. Just having a large set of statistics taken from the dataset (which seems rather small) and making a large Bayesian Network to crunch out the probability of needing medical care in a given time frame seems to be the best solution to the problem.<p>I am interested in seeing other views on these points. heavens, I might learning something about a field I am a dilettante in from a master. (ironically this is more the goal then being "right" is)
Why this will save money:<p><i>The Hot Spotters - Can we lower medical costs by giving the neediest patients better care?</i> by Atul Gawande<p><a href="http://www.newyorker.com/reporting/2011/01/24/110124fa_fact_gawande?currentPage=all" rel="nofollow">http://www.newyorker.com/reporting/2011/01/24/110124fa_fact_...</a><p>On HN: <a href="http://news.ycombinator.com/item?id=2154579" rel="nofollow">http://news.ycombinator.com/item?id=2154579</a>
The benefits of finding these folks are many:<p><a href="http://kottke.org/11/01/controlling-healthcare-costs-by-focusing-on-the-neediest-patients" rel="nofollow">http://kottke.org/11/01/controlling-healthcare-costs-by-focu...</a>
"training dataset includes several thousand anonymized patients and will be made available"<p>That seems like an awfully small dataset. It also doesn't look like it is limited to one disease which would make the search space enormous especially if all the patients didn't have the same labs drawn!<p>If it was completely standardized data several thousand may be sufficient to train, but I think they are looking for something more 'magic' than that.
Oooh. This is just begging for a privacy firestorm when someone de-anonymizes the data, which I'm guessing won't be super hard given the kind of medical features they'd need to provide to make this task useful.
Without a legal protection in place which disallows something like this for deciding the insurance rates, this sounds like something which can get abused.<p>But I think it is better that is happens in public via an open competition rather then in a private research group funded by an insurance company. At least, everyone will immediately know what can be predicted rather then finding it out through a class action suit years later.
sorry, I don't think being born in the west entitles you to millions of dollars of medical care at other's expense when a million dollars means hundreds of lives saved.
Doctors can tell you which patients will be back, the problem is they can't, because if they do, that will be discrimination which would be grounds for burning the doctor at the stake. The software which does exactly the same thing, however, can't be burned at the stake for discrimination because in the event where the guilty party cries fowl, you simply print out the math. It's genius.<p>You 1% (repeat sickly offenders) causing 30% of the medical care costs better get ready to pay your increased share to acquire that care. If it can be determined that one human would likely need 10 million dollars of medical care (on account of heavily defective dna) and another human will likely need only 200 thousand (flawless dna), the one who is likely to need more should be paying more.