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I think personal health monitoring is probably the most important thing on that list. The thing that excited me the most when smartphones started becoming popul

by ericd 15y ago

I think personal health monitoring is probably the most important thing on that list. The thing that excited me the most when smartphones started becoming popular was the prospect that they could coordinate data collection from a number of sensors always collecting data - basic ones like Nike+, but perhaps also sensors measuring sleep, taking periodic bloodwork, etc. At the same time, perhaps you could automatically monitor personal behavior such as foods eaten. Personal diagnostics would be an important use of that, but I think more importantly, with a very large public dataset of basic biometric data correlated with behavior data and medical results across a significant portion of the population, we could stop treating human health studies as bespoke one-offs put on at great expense and start treating them as data mining problems. You could begin to spot correlations between behaviors and results that are unintuitive given conventional wisdom. I think that the resulting burst of discoveries would be on par with any of history's great scientific revolutions.

rdl 15y ago
Scott Adams (Dilbert guy) proposed this a few years ago as a way to win the Nobel Prize in Physiology or Medicine. Being able to "tivo rewind real life" would be pretty amazing -- watch a bunch of things passively, record data, notice a spike in mortality, then find the common factors and stop the new plague (or the kids who found a Cobalt-60 source, or the pump infected with Cholera).
- ericd 15y ago
  I think it would bring about enough breakthroughs for a lot more than one Nobel :-) But yeah, I'm definitely not the first one to fantasize about being able to correlate behaviors or even just basic bio signs across a big chunk of the population with the medical results. I even think it'd be feasible to pull it together as a company, since there are lots of immediate potential benefits to using the things that would collect this data. A much better replacement for Life Alert, which works even if you're unconscious and can't press the button. Dieting aids, sleep aids, exercise aids, passive diabetes monitoring. It's a lot to bite off, especially as a startup, but I think a team that knew how to execute on this kind of product could roll out products serially and pull together that dataset. That dataset would enhance each other product in the way that each of Google's views on the internet (DNS traffic, browser feedback, analytics) help its core product. A hell of a defensible advantage. Not sure how one could convince that company to give up its crown jewels for the sake of medical research, though :-)
aidos 15y ago
Interestingly I have a friend (a hematologist) that says that they actually try not to overscan for fear of finding something. There are plenty of cases where a scan reveals something that may not manifest itself into a noticeable issue in the lifetime of the patient but they then need to treat. Unfortunately often the treatments themselves are invasive (sometimes far more so that the issue itself might have been). I guess that problem will diminish as advancements in medical diagnosis and treatment progress.
tomp 15y ago
There is a fundamental difference between a scientific study and data-mining. Science is based on probability theory. Until we discover the "grand theory of everything", out other theories will be only approximate, and out experimental results not 100% predictable. Therefore, scientists consider a prediction as correct if the chance of predicting something at random is less than some probability, usually 10%, 5% or 1%. However, for this to work, each study must be based on new data. If you use the same data to check e.g. 10 predictions, each of which has 10% chance of happening even if incorrect, you will in average confirm 1 of your predictions, even if all are incorrect!
- _delirium 15y ago
  It's also fairly easy to, even unwittingly, begin tuning some of your procedures to the data set (i.e. overfitting), even if you're employing the usual precautions, like cross-validation. As an extreme example, if we had one gigantic fixed data set and gave researchers 10 years to work on it, by the end of that 10 years there would be entire techniques at least partially specialized to that specific dataset, with poor generalization outside of it.
- lutorm 15y ago
  No, if 10 predictions each have a 10% chance of a false positive, you will always have on average 1 false positive. Whether you test them on the same data or not doesn't matter (unless 10 predictions test the same thing, of course.) What you need is a control sample that you know should be negative, so you can actually measure the false positive rate. (But with a sufficiently large base sample, you can look for correlations in small subsamples and use the whole sample as a control.)
  - tomp 15y ago
    > No, if 10 predictions each have a 10% chance of a false positive, you will always have on average 1 false positive. Whether you test them on the same data or not doesn't matter That's true. I should have said it differently. In fact, I'm not even sure that my understanding is correct. The problem with not using new data to test each new prediction is, that if a scientist wants to show A on data X, but data X doesn't confirm A, the scientist modifies A slightly and now tests A' on X, which is again rejected, and then modifies it again, testing A'' on X, and so on... until the data X actually confirms hypothesis A'''''''! That's the real problem - using data without a predefined plan for how you will use this data. In the above example, the data that you collected affected your decision-making process, so your results are not independent of the data (and thus not replicable!).

by ericd 15y ago

rdl 15y ago

Scott Adams (Dilbert guy) proposed this a few years ago as a way to win the Nobel Prize in Physiology or Medicine. Being able to "tivo rewind real life" would be pretty amazing -- watch a bunch of things passively, record data, notice a spike in mortality, then find the common factors and stop the new plague (or the kids who found a Cobalt-60 source, or the pump infected with Cholera).

ericd 15y ago
I think it would bring about enough breakthroughs for a lot more than one Nobel :-) But yeah, I'm definitely not the first one to fantasize about being able to correlate behaviors or even just basic bio signs across a big chunk of the population with the medical results. I even think it'd be feasible to pull it together as a company, since there are lots of immediate potential benefits to using the things that would collect this data. A much better replacement for Life Alert, which works even if you're unconscious and can't press the button. Dieting aids, sleep aids, exercise aids, passive diabetes monitoring. It's a lot to bite off, especially as a startup, but I think a team that knew how to execute on this kind of product could roll out products serially and pull together that dataset. That dataset would enhance each other product in the way that each of Google's views on the internet (DNS traffic, browser feedback, analytics) help its core product. A hell of a defensible advantage. Not sure how one could convince that company to give up its crown jewels for the sake of medical research, though :-)

aidos 15y ago

Interestingly I have a friend (a hematologist) that says that they actually try not to overscan for fear of finding something. There are plenty of cases where a scan reveals something that may not manifest itself into a noticeable issue in the lifetime of the patient but they then need to treat. Unfortunately often the treatments themselves are invasive (sometimes far more so that the issue itself might have been). I guess that problem will diminish as advancements in medical diagnosis and treatment progress.

tomp 15y ago

There is a fundamental difference between a scientific study and data-mining. Science is based on probability theory. Until we discover the "grand theory of everything", out other theories will be only approximate, and out experimental results not 100% predictable. Therefore, scientists consider a prediction as correct if the chance of predicting something at random is less than some probability, usually 10%, 5% or 1%. However, for this to work, each study must be based on new data. If you use the same data to check e.g. 10 predictions, each of which has 10% chance of happening even if incorrect, you will in average confirm 1 of your predictions, even if all are incorrect!

_delirium 15y ago
It's also fairly easy to, even unwittingly, begin tuning some of your procedures to the data set (i.e. overfitting), even if you're employing the usual precautions, like cross-validation. As an extreme example, if we had one gigantic fixed data set and gave researchers 10 years to work on it, by the end of that 10 years there would be entire techniques at least partially specialized to that specific dataset, with poor generalization outside of it.
lutorm 15y ago
No, if 10 predictions each have a 10% chance of a false positive, you will always have on average 1 false positive. Whether you test them on the same data or not doesn't matter (unless 10 predictions test the same thing, of course.) What you need is a control sample that you know should be negative, so you can actually measure the false positive rate. (But with a sufficiently large base sample, you can look for correlations in small subsamples and use the whole sample as a control.)
- tomp 15y ago
  > No, if 10 predictions each have a 10% chance of a false positive, you will always have on average 1 false positive. Whether you test them on the same data or not doesn't matter That's true. I should have said it differently. In fact, I'm not even sure that my understanding is correct. The problem with not using new data to test each new prediction is, that if a scientist wants to show A on data X, but data X doesn't confirm A, the scientist modifies A slightly and now tests A' on X, which is again rejected, and then modifies it again, testing A'' on X, and so on... until the data X actually confirms hypothesis A'''''''! That's the real problem - using data without a predefined plan for how you will use this data. In the above example, the data that you collected affected your decision-making process, so your results are not independent of the data (and thus not replicable!).