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Google Is Training Machines to Predict When a Patient Will Die

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Re: Google Is Training Machines to Predict When a Patient Will Die

#41

Post by roenxi »

bane wrote: post #17
baxtr wrote: post #7

A very close relative of mine passed away 2 weeks ago. She had cancer for 2 years and when she was first diagnosed the doctors would give her 8-10 “good years”. Well... When she died, I was shocked to learn that very few data points of her sichkness and treatment history would be preserved for later analysis. Doctors work almost entirely on their gut feeling and probably some clinical studies with n being very small.…

I get the impression that much of medicine is one of those strange practices that looks kinda like science, but really isn't. Medical practice appears to work more like a highly evolved guild system than any other kind of recognizable scientific activity. It's probably far beyond the point where its necessary to bring in actual scientific practices, so I can't say it's on the cusp of a sweeping change. One of the pro…

> I get the impression that much of medicine is one of those strange practices that looks kinda like science, but really isn't.

It is very hard to escape from the fact that there are patients who are so difficult to keep alive that the rational choice is probably to let them die. The exact circumstances will depend on your value structure, but once that is settled any scientific approach that optimises for greatest net good or quality of life will involve letting some patients go without really trying to save them.

There is a pretty substantial lobby (no particular affiliation) who just won't accept that attitude being out in public.

I watch the struggles of data science to take root in our medical system with great interest in light of this. Anyone using a statistical argument to justify inaction when confronted with a patient is running a risk of being pilloried. Pictures of ancient grandmas on the front page of a newspaper style stuff. And that is if they get the stats right - get something wrong and then you feel like a loser on top of running that risk.

Between that and the serious privacy concerns, I can believe that the medical world will be mysteriously disorganised and ad-hoc in their decision making no matter what statistics available. I don't see what the real incentive is to look at and use the numbers, without having a statesman like dedication to the greater good, come what may. Such persons combined with leadership and statistical knowhow are rare.

Re: Google Is Training Machines to Predict When a Patient Will Die

#42

Post by toomuchtodo »

louden wrote: post #33
digitalzombie wrote: post #12

Why did they just use a logistic model instead of survival/time-to-event model? https://www.ncbi.nlm.nih.gov/pubmed/21478775 Uses cox regression model which is a survival regression model. Also the base model aka previous model they're comparing it to is a logistic regression and the link leads to a pdf about how to increase hospital efficiency it seems like. This sounds stupid and heartless. In statistic we got surv…

> It's great but with healthcare I don't think people should be treated as dollar signs. These kinds of models are also great for triage. Healthcare is a limited resource, especially in trauma situations which have been using models to measure survival for decades.

At the same time, we should be finding ways to use technology so healthcare isn’t a limited resource, so humans aren’t the bottleneck.

Re: Google Is Training Machines to Predict When a Patient Will Die

#43

Post by HillaryBriss »

aaavl2821 wrote: post #28

This was a year ago so things may have changed, but a senior exec at one of the nations largest health systems told me they were looking to change their end of life care program and looked at dozens of AIs that predicted when patients would die. None of them came close to just asking doctors which patients they thought would die within 12-18 months The point of the project was to make end of life easier for patients…

> None of them came close to just asking doctors ...

at a conference, i once heard that the best single predictor of ICU patient mortality was reduction of doctor attentiveness. in other words, patients in ICUs whom doctors began to visit less, treat less, basically start to ignore -- those patients were the ones for whom death was imminent.

the challenge is that it's hard to untangle cause and effect. was it a self-fulfilling prophecy? etc.

Re: Google Is Training Machines to Predict When a Patient Will Die

#44

Post by Arnt »

Rainymood wrote: post #22
digitalzombie wrote: post #12

Why did they just use a logistic model instead of survival/time-to-event model? https://www.ncbi.nlm.nih.gov/pubmed/21478775 Uses cox regression model which is a survival regression model. Also the base model aka previous model they're comparing it to is a logistic regression and the link leads to a pdf about how to increase hospital efficiency it seems like. This sounds stupid and heartless. In statistic we got surv…

>I don't think people should be treated as dollar signs. You're a bit late to the party, no? Basically mortality calculations and risk transfer is what the whole insurance business is based upon.

That reply is a bit factual... I want to talk about why it came to be this way.

Medicine has advanced to a point where some serious illnesses can be kept at bay for decades. People with some formerly mortal diseases can get medicines that cost $100k/year and/or professional help every day, and live well for thirty years.

This means that each of rich societies has to choose:

1. Raise health insurance rates until the budget covers everything that's possible medically. However, in some rich countries, people's after-tax income is only a few doublings above the current health care plus pension costs...

2. Decide that some treatments aren't worthwhile, ie. everyone with Hyperthis or Abnormalthat Syndrome gets cheap palliative treatment and a peaceful, gentle death.

3. Decide on a per-patient basis, often involving numbers such as "cost of treatment" and "years left of productive life".

Option 1 is the humane way, but slightly impossible. Options 2 and 3 involve treating people like dollar signs, one way or another. It's an unpleasant choice, not an avoidable one.

Re: Google Is Training Machines to Predict When a Patient Will Die

#45

Post by louden »

toomuchtodo wrote: post #42
louden wrote: post #33

Earlier quoted context omitted.

> It's great but with healthcare I don't think people should be treated as dollar signs. These kinds of models are also great for triage. Healthcare is a limited resource, especially in trauma situations which have been using models to measure survival for decades.

At the same time, we should be finding ways to use technology so healthcare isn’t a limited resource, so humans aren’t the bottleneck.

Even when we have whatever technology would make heathcare less limited, there will need to be ways to measure the prognosis of the patient. Otherwise we would be giving very unpleasant treatments to patients who only need palliative care (like chemo for terminal cancer patients).

Re: Google Is Training Machines to Predict When a Patient Will Die

#46

Post by taneq »

bane wrote: post #17
baxtr wrote: post #7

A very close relative of mine passed away 2 weeks ago. She had cancer for 2 years and when she was first diagnosed the doctors would give her 8-10 “good years”. Well... When she died, I was shocked to learn that very few data points of her sichkness and treatment history would be preserved for later analysis. Doctors work almost entirely on their gut feeling and probably some clinical studies with n being very small.…

I get the impression that much of medicine is one of those strange practices that looks kinda like science, but really isn't. Medical practice appears to work more like a highly evolved guild system than any other kind of recognizable scientific activity. It's probably far beyond the point where its necessary to bring in actual scientific practices, so I can't say it's on the cusp of a sweeping change. One of the pro…

The simple fact that "evidence-based medicine" needs a special name to describe it, rather than all medicine being evidence-based, is still absolutely mindblowing to me.

Re: Google Is Training Machines to Predict When a Patient Will Die

#47

Post by kgwgk »

Arnt wrote: post #44
Rainymood wrote: post #22

Earlier quoted context omitted.

>I don't think people should be treated as dollar signs. You're a bit late to the party, no? Basically mortality calculations and risk transfer is what the whole insurance business is based upon.

That reply is a bit factual... I want to talk about why it came to be this way. Medicine has advanced to a point where some serious illnesses can be kept at bay for decades. People with some formerly mortal diseases can get medicines that cost $100k/year and/or professional help every day, and live well for thirty years. This means that each of rich societies has to choose: 1. Raise health insurance rates until the b…

> slightly impossible

Nice way to put it.

Re: Google Is Training Machines to Predict When a Patient Will Die

#48

Post by sidrajaram »

digitalzombie wrote: post #12

Why did they just use a logistic model instead of survival/time-to-event model? https://www.ncbi.nlm.nih.gov/pubmed/21478775 Uses cox regression model which is a survival regression model. Also the base model aka previous model they're comparing it to is a logistic regression and the link leads to a pdf about how to increase hospital efficiency it seems like. This sounds stupid and heartless. In statistic we got surv…

Their model was not logistic regression (the networks may have had a few logistic units in it, but it's hard to call that a logistic model). The logistic models they compared against were published models from the academic literature. I'm not enough of an expert in this specific subdomain to comment on whether these were the benchmark papers or not.

I don't know if you're trying to imply that the authors of this paper didn't know/know of survival analysis, or if it was a general rant. Looking at the names I know on the paper and the affiliations/backgrounds of the others, it's safe to say they are aware of proportional hazards models.

Survival analysis is not called for when predicting the outcome variables of interest in this study, and that seems to be your primary beef - that they chose the wrong outcomes to model in order to "make hospitals money". I would think that being able to predict outcomes help hospitals plan and manage their resources effectively. From your high horse this may appear to be a wasteful endeavor, but controlling costs will do much more to save lives by making healthcare accessible, rather than building survival analysis models for rare diseases that affect some trivially small portion of the population.

The truth is outside of tech, statisticials (or data scientists) are way underpaid relative to the training and specialization demanded of them. This is true for non-profits and academia. Note that administrators in both these fields are not underpaid to the same degree. Instead of money, they are expected to pay their bills with warm fuzzy feelings of doing good for the world, because of attitudes like the ones expressed in your comment.

Also, fun fact: survival analysis was developed for actuarial use to make ugh money, not bio/medical statistics.

Re: Google Is Training Machines to Predict When a Patient Will Die

#49

Post by [deleted] »

louden wrote: post #45
toomuchtodo wrote: post #42

Earlier quoted context omitted.

At the same time, we should be finding ways to use technology so healthcare isn’t a limited resource, so humans aren’t the bottleneck.

Even when we have whatever technology would make heathcare less limited, there will need to be ways to measure the prognosis of the patient. Otherwise we would be giving very unpleasant treatments to patients who only need palliative care (like chemo for terminal cancer patients).

[deleted]

Re: Google Is Training Machines to Predict When a Patient Will Die

#50

Post by notahacker »

amelius wrote: post #2

> Google’s system even showed which records led it to conclusions. What if the system points out that the particular doctors which appear in the records are the culprit?

The UK had a big case where the question asked was why they didn't spot the doctor with the unusually large number of patients who died unexpectedly, when a cursory examination of his approach to their medical records might have uncovered evidence that many of those deaths weren't natural.

https://en.wikipedia.org/wiki/Harold_Shipman

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