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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

#51

Post by tialaramex »

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.…

Yup. Doctors interested in the philosophy of science have moaned about this sort of thing before. A more typical example goes like this:

I'm a GP, and I see dozens of patients every year with cases of Foozle disease. I was taught in medical school to give them orange pills, but I follow developments in general medicine and I know the new purple pill has seen good results for Foozle too. Nobody has published even a small study about which is better.

The law says since a responsible doctor wouldn't know one is better I can choose either. I could give all my female Foozle patients the orange pills and prescribe the purple for everybody else, or I could just pick whichever is cheapest, or I could even pick randomly for each patient. All legal.

But, if I pick randomly AND record the results, thus doing science to find out which was best, that's an unauthorised medical experiment and I could lose my job or even go to jail.

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

#52

Post by Symmetry »

jand wrote: post #4

I would feel quite unconfortable if every action taken (or not taken) by medic staff has to be second-guessed simply because there are financial incentives derived from this research. Customer A does not receive full medical support because "he's dying anyway"? Is there a way to ensure that such cruel use of the research is prohibited?

Heroic efforts on futile attempts to keep dying patients alive have a huge cost in human suffering and we, as a society, really ought to stop making them.

http://slatestarcodex.com/2013/07/17/who-by-very-slow-decay/

http://www.zocalopublicsquare.org/2011/11/30/how-doctors-die...

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

#53

Post by knuththetruth »

Maybe this could be useful in countries with nationalized healthcare, but it seems a terrible idea to deploy in the for-profit monstrosity of American healthcare. Institutions will immediately begin to deferring to the “objective” judgements of such systems to either pull care to save costs, or, as was the case with that hospice provider in Texas, kill sick people for more money:

https://www.cbs19.tv/mobile/article/news/north-texas-hospice...

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

#54

Post by LyndsySimon »

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…

You've missed the way that it's done absent state intervention, which is perhaps a modification of #3:

    Decide on a per-patient basis, treating the illness when the individual or their family can afford the cost of treatment.

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

#55

Post by LyndsySimon »

hprotagonist wrote: post #5

As much as 80 percent of the time spent on today’s predictive models goes to the “scut work” of making the data presentable, said Nigam Shah, an associate professor at Stanford University, who co-authored Google’s research paper, published in the journal Nature. Google’s approach avoids this. "You can throw in the kitchen sink and not have to worry about it,” Shah said. Alarm bells start ringing right about here. A s…

> A solid 90% of AI is data munging

What's the other 10%?

I'm serious - I've got a better-than-average understanding of statistics, and everything I've seen referred to as "AI" seems to really be just a statical model with a thin layer of code over the top to make decisions based upon it on the fly. Is there more to the state of the art that isn't apparent from the outside?

In fact, the only thing that comes to mind that doesn't fit that description would be genetic algorithms, but I don't hear much about them these days.

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

#56

Post by knuththetruth »

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…

Option 4:

Decommodify every aspect of healthcare, from the education of doctors to the hospitals themselves. Invest massively in basic medical science research and provide the corresponding advancements and technologies free of charge to the healthcare system.

These treatments cost 100K because of the economic arrangements from which they emerge, not because that’s some inherent (or even sane) price. For a more concrete contrast, Japan has a healthcare system based almost entirely on government set prices for procedures and has better outcomes than the US.

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

#57

Post by evandijk70 »

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.…

As a researcher, this is absolutely true and incredibly frustrating.

I am trying to make the survival data of a clinical trial combined with sequencing data of the patients publicly available. Nearly all the patients have died by now, as the patients in this cohort patients have a very poor prognosis (median survival of about 24 months). It is still almost impossible to release clinical data (diagnosis, number and location of metastasis, age of patients, etc) combined with sequencing data without violating privacy laws.

In the meantime, there is almost no career incentive for me to make the data available. I do not have, and am unlikely to get a permanent contract. In my next job interview I will be judged by the number of publications on my CV.

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

#58

Post by [deleted] »

LyndsySimon wrote: post #55
hprotagonist wrote: post #5

As much as 80 percent of the time spent on today’s predictive models goes to the “scut work” of making the data presentable, said Nigam Shah, an associate professor at Stanford University, who co-authored Google’s research paper, published in the journal Nature. Google’s approach avoids this. "You can throw in the kitchen sink and not have to worry about it,” Shah said. Alarm bells start ringing right about here. A s…

> A solid 90% of AI is data munging What's the other 10%? I'm serious - I've got a better-than-average understanding of statistics, and everything I've seen referred to as "AI" seems to really be just a statical model with a thin layer of code over the top to make decisions based upon it on the fly. Is there more to the state of the art that isn't apparent from the outside? In fact, the only thing that comes to mind…

[deleted]

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

#59

Post by louden »

chii wrote: post #29
joshgel wrote: post #15

Earlier quoted context omitted.

This is partially true. Her data can't be used willy nilly (sp?). Hospitals and doctors offices can use this data for their internal operations, to improve efficiency, quality, etc and (honestly) profit. They can also use this data in research, but only with IRB approval for exemption from consent. Her data should be protected! Also, I would add that the data collected for routine patient care is often of significant…

> Her data should be protected! but if it could be anonymized (i.e., the medical data such as blood tests, history of diseases etc disassociated with any names), then wouldn't it make sense to have this data be used for research?

Her consent would also be needed.

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

#60

Post by everdev »

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.…

Sorry for your loss.

When it's the other way around and someone survives 10 years on a "2 years left to live" diagnosis, we seem quick to credit their strength, determination, spirituality, etc., rather than looking at how inaccurate the original prediction might have been.

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