14 ms·

Google Is Training Machines to Predict When a Patient Will Die

68 points by hawkilt 8y ago

jonkiddy 8y ago
I work for a cancer research hospital. I'm training an LSTM today using TensorFlow to determine months left to live for specific patient conditions given previous outcomes.
itschekkers 8y ago
I hate to pour cold water on this, but medical researchers have been predicting mortality for years. For example, here's a paper from the 1980's also predicting when a patient will die, also using a couple of thousands of patients' data: http://europepmc.org/abstract/med/3816253 http://europepmc.org/abstract/med/3816253 And, Google's paper wasn't published in Nature, it was in a new open access journal owned by Nature. In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company).
- himom 8y ago
  Regardless of medical / health insurance purposes, actuaries have been doing this for over a century esp. to price life insurance, among other things.
- carlmr 8y ago
  >In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company). I think you want to say Skoda or Seat. Both owned by VW and the cheap cars.
- joker3 8y ago
  Google's innovation isn't predicting mortality. It's predicting mortality based on data that was hidden from previous models. That's very likely where all of the improvement is coming from, but since it's hard to write a good headline or lede based on that, we get this instead.
- muraiki 8y ago
  From the paper: "...the novelty of the approach does not lie simply in incremental model performance improvements. Rather, this predictive performance was achieved without hand-selection of variables deemed important by an expert, similar to other applications of deep learning to EHR data. Instead, our model had access to tens of thousands of predictors for each patient, including free-text notes, and identified which data were important for a particular prediction." So it sounds like the advance here is actually in the following: "a generic data processing pipeline that can take raw EHR data as input, and produce FHIR outputs without manual feature harmonization". The article doesn't explain this very clearly. Yay!
pjmlp 8y ago
I am sure Deutsch Bank will appreciate such "improvements". http://www.spiegel.de/international/business/short-selling-american-lives-deutsche-bank-life-insurance-fund-in-hot-water-a-662447.html http://www.spiegel.de/international/business/short-selling-a...
himom 8y ago
With deep learning, actuaries were inevitably, mostly obsolete, at some point. It’s unpleasant to think, that in a way, tech can now readily disrupt nearly any arbitrary office-worker knowledge industry at will, destroying businesses and specialties and concentrate both capability and wealth in ever fewer hands. Coding isn’t a “forever” specialty either... self-coding machines will create languages, protocols and systems humans will likely be unable to understand, limit, modify, monitor or audit.
aaavl2821 8y ago
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 and families. Many patients get transferred between multiple facilities in their last few months, and few end up passing in a place and manner they'd like. This project aimed to ask patients and their families how they'd like to spend their last few months. Initially the health system resisted this bc they wanted to keep patients in the hospital to make money off them, but then they realized these patients were in the hospital so long they weren't profitable. Then the plan was adopted quickly
- segmondy 8y ago
  Pretty much it comes down to profit.
- HillaryBriss 8y ago
  > 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.
amelius 8y ago
> 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?
- Angostura 8y ago
  Uncomfortable, but over all a good thing as long as the system uses this information properly; offering doctors additional supportive training, if appropriate. (And not forgetting that the doctors with the the worst recovery rates may be the ones predisposed to take the trickiest cases).
  - carlmr 8y ago
    >(And not forgetting that the doctors with the the worst recovery rates may be the ones predisposed to take the trickiest cases). IIRC there are a lot of surgeons not taking on cases already because of the risk involved to their career. This might exacerbate it if the data is used blindly.
- notahacker 8y ago
  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 https://en.wikipedia.org/wiki/Harold_Shipman
coldseattle 8y ago
This will help with self-driving cars faced with a "trolley problem." You can choose the victim most likely to die anyway.
- dvdhnt 8y ago
  You forgot the /s flag.
- kypro 8y ago
  The trolley problem here already exists regardless. Just because making difficult decisions is uncomfortable doesn't mean the most moral outcome is to leave it to chance.
jand 8y ago
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?
- newsbinator 8y ago
  More data is good. It's better if this becomes an ethical/moral problem about when to care for patients and to what degree, rather than a guessing game problem while anybody's guess is as good as anybody else's.
- dapearce 8y ago
  >Customer A does not receive full medical support because "he's dying anyway"? Unless the patient consents to stop or reduce care, this would be medical negligence, which is illegal (in the US). Medical providers are required by law to provide medical treatment with all the knowledge and skill they possess.
  - dragonwriter 8y ago
    > Medical providers are required by law to provide medical treatment with all the knowledge and skill they possess. No, to avoid liability for malpractice, they need to meet the “professional standard of care”, which is assessed by general practice in the medical community, not “all the knowledge and skill” the individual provider possesses.
    - deleted 8y ago
      [deleted]
  - dekhn 8y ago
    This is not factually correct.
  - Broken_Hippo 8y ago
    I wish that were the case. They'll treat you in the ER, but not make sure you can actually purchase antibiotics to cure the thing that brought you to the ER to begin with, nor make sure you can afford the bone specialist you need to see for your surgery. Anything short of an emergency and you can be denied care due to finances. Even if your problem is cancer or they know it will eventually be an emergency or life-threatening. Money is part of the reason folks will use the emergency room instead of urgent care: Urgent care requires upfront payment, the emergency room does not. Many doctors will refuse to see someone if they do not pay upfront. Luckily, different agencies tend to step in with end of life care.
hprotagonist 8y ago
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 solid 90% of AI is data munging, so that work isn't going away even a little bit; historically, Google throwing the kitchen sink into healthcare problems with lots of noisy data leads to flu seasons being correlated with softball victories or other wildly spurious correlations.
- LyndsySimon 8y ago
  > 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.
  - deleted 8y ago
    [deleted]
baxtr 8y ago
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 hate that she died, but it’s even worse to know that all her data died with her and won’t help any other patient. I think this is one of the rare cases where collecting more data would help protect people
- pavel_lishin 8y ago
  I'm sorry to hear about your loss, but can you elaborate on what data was discarded after her passing?
  - baxtr 8y ago
    Thank you. Well, the way I understand it: her treatment history won’t be considered by any other doctor than her own. The reason for that is that she wasn’t part of a proper medical study. However, I believe there is value in her data. The last days she was treated with an experimental drug. If there was an open database on all cases with treatment history I could have looked into that and get a feeling what to expect. These things often come with huge side effects, so you want to do it only if there is a slight chance. Also, if this data was collected globally, the number of reference cases would be much larger.
    - joshgel 8y ago
      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 significantly lesser quality than that collected as part of organized research studies. This is because in a research study, all patients get the same studies and tests whereas in clinical care, they only get what they need, so there is significant amounts of 'missing' data.
digitalzombie 8y ago
Why did they just use a logistic model instead of survival/time-to-event model? https://www.ncbi.nlm.nih.gov/pubmed/21478775 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 survival analysis, a whole branch that is focus on patient and their survival rate for the medical field. Google chose to compare to a paper and algorithm that focus on what seems like making hospital more money instead. I've seen a lot of data science people goes into different field and just telling people they can make money for them. It's great but with healthcare I don't think people should be treated as dollar signs. If anybody up and coming wants to use data science in bio, I would encourage them to look into statistic and biostatistic. We have tons of stuff already and then branch out to ML later. But at least know what's out there and there are establish organization, nonprofit out there too that all they do is biostat and build model. My friend works at a nonprofit child oncology. I just want to point out there are people that's building model to help patient with terrible sickness out there to survive. We're not diddling our thumbs trying to make other people richer.
- louden 8y ago
  > 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.
  - toomuchtodo 8y ago
    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.
    - louden 8y ago
      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).
waydowntogo 8y ago
I'm programmer and I'm always saying (something like this) - the program / AI can be able to do things the best way how it can be done the best way by its programmer(s) My point is - this is completely nonsense. IT world and Real world are two difference worlds. It's look like Google needs wake up call.
- ukj 8y ago
  Which is why positive AND negative feedback loops from your users/customers/<people whose lives your software will affect> are vital to building great software.
neurobashing 8y ago
I'm sure it's an interesting technical challenge with huge amounts of complex, nuanced debate involved, and it could have a huge impact on health care. That said, my wife is an oncology nurse, and I will bet everything that no machine will ever get better at predicting than an experienced, skilled nurse. Humans are built to read humans.
- adrianN 8y ago
  "ever" is a pretty long time though.
- evandijk70 8y ago
  I would be very willing to take that bet on a 30 year horizon Just think about the machines/algorithms your wife already (indirectly) uses to predict outcome for patients. - An MRI scan, which is created from physical laws (Maxwell's law together with a quantum mechanical understanding of hydrogen atoms) and reconstructs a 3D-image of the different tumors. Knowing if and where metastasis are present have an enormous impact on the prognosis. - The algorithms used to align sequencing reads from biopsies to determine the mutation status, which are critical to determine the prognosis of some tumors. In fact, I'd wager that if your wife did not have access to these algorithms, she would already perform worse. And, in my opinion, the distinction between machine learning and the algorithms used to reconstruct 3D-images or align sequences is somewhat arbitrary.
- mcny 8y ago
  » my wife is an oncology nurse Have you ever asked her if she wants to do the job of predicting when a patient will die? How many hours would she need to spend reading the patient's history to come up with a prediction she feels satisfied to use to decide what service the patient will get? I'm not a medical professional but my gut reaction is I don't want to do anything with predicting or sentencing if some machine can do it almost as well as I can. I remember this story of a radiologist who told me he thinks we spend too much money for too little during the last about six months of a patient's life. If we had better information on when the last six months starts, maybe we could reduce the cost of healthcare? Apparently, we spend cost to 18% of GDP on healthcare in the US? Iirc, most of Europe is closer to 12?
b34r 8y ago
I’m sure insurance companies will be glad to get their hands on this to use it as an excuse to deny coverage.
- Eridrus 8y ago
  Insurance is all about pricing risk accurately, so yes, of course, these ideas will be used to price the risk more accurately. The problem isn't that insurers are horrible people, it's that insurance is a bad fit for healthcare.
  - Spivak 8y ago
    Why do you say that? Take insurance in its most basic form: a company does its best to determine how much money they're likely going to pay out over the lifetime of a policy and spreads that cost out for their customer who is then insulated from unexpected expenses or non-uniform cost schedules. It seems perfectly suited to medicine. Insurance doesn't solve the problem of a population where the total cost of care is more then they can collectively afford or when an individual's treatment has known costs higher than what they can afford but nobody has a good answer for that. Insuring everyone from birth can be a solution for the individual but not the population.
    - Eridrus 8y ago
      Insurance works when you incur a lot of one time costs, it works much worse when you start incurring a lot of expenses over a long period of time. Ideally you could buy insurance that would cover all of your long term costs once you contracted some disease, but this doesn't exist in the US. And doubly doesn't exist when you realise that there are a bunch of forces incentivizing people to get their insurance from an employer.
      - Spivak 8y ago
        > you could buy insurance that would cover all of your long term costs once you contracted some disease Kind of defeats the point if you're using it to cover known future costs. Insurance can really only insulate you against risk, it's not free money.
codetrotter 8y ago
- “Hey Google! How long do I have left to live?” - “Are you sure you want to know?” - “...”
knuththetruth 8y ago
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-nurse-overdosed-clients-for-money/501-564776570 https://www.cbs19.tv/mobile/article/news/north-texas-hospice...
sireat 8y ago
How soon until Lifeline is a reality for a subset of patients? https://en.wikipedia.org/wiki/Life-Line https://en.wikipedia.org/wiki/Life-Line

68 points by hawkilt 8y ago

I work for a cancer research hospital. I'm training an LSTM today using TensorFlow to determine months left to live for specific patient conditions given previous outcomes.

itschekkers 8y ago

I hate to pour cold water on this, but medical researchers have been predicting mortality for years. For example, here's a paper from the 1980's also predicting when a patient will die, also using a couple of thousands of patients' data: http://europepmc.org/abstract/med/3816253 http://europepmc.org/abstract/med/3816253 And, Google's paper wasn't published in Nature, it was in a new open access journal owned by Nature. In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company).

himom 8y ago
Regardless of medical / health insurance purposes, actuaries have been doing this for over a century esp. to price life insurance, among other things.
carlmr 8y ago
>In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company). I think you want to say Skoda or Seat. Both owned by VW and the cheap cars.
joker3 8y ago
Google's innovation isn't predicting mortality. It's predicting mortality based on data that was hidden from previous models. That's very likely where all of the improvement is coming from, but since it's hard to write a good headline or lede based on that, we get this instead.
muraiki 8y ago
From the paper: "...the novelty of the approach does not lie simply in incremental model performance improvements. Rather, this predictive performance was achieved without hand-selection of variables deemed important by an expert, similar to other applications of deep learning to EHR data. Instead, our model had access to tens of thousands of predictors for each patient, including free-text notes, and identified which data were important for a particular prediction." So it sounds like the advance here is actually in the following: "a generic data processing pipeline that can take raw EHR data as input, and produce FHIR outputs without manual feature harmonization". The article doesn't explain this very clearly. Yay!

pjmlp 8y ago

I am sure Deutsch Bank will appreciate such "improvements". http://www.spiegel.de/international/business/short-selling-american-lives-deutsche-bank-life-insurance-fund-in-hot-water-a-662447.html http://www.spiegel.de/international/business/short-selling-a...

himom 8y ago

With deep learning, actuaries were inevitably, mostly obsolete, at some point. It’s unpleasant to think, that in a way, tech can now readily disrupt nearly any arbitrary office-worker knowledge industry at will, destroying businesses and specialties and concentrate both capability and wealth in ever fewer hands. Coding isn’t a “forever” specialty either... self-coding machines will create languages, protocols and systems humans will likely be unable to understand, limit, modify, monitor or audit.

aaavl2821 8y ago

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 and families. Many patients get transferred between multiple facilities in their last few months, and few end up passing in a place and manner they'd like. This project aimed to ask patients and their families how they'd like to spend their last few months. Initially the health system resisted this bc they wanted to keep patients in the hospital to make money off them, but then they realized these patients were in the hospital so long they weren't profitable. Then the plan was adopted quickly

segmondy 8y ago
Pretty much it comes down to profit.
HillaryBriss 8y ago
> 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.

amelius 8y ago

> 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?

Angostura 8y ago
Uncomfortable, but over all a good thing as long as the system uses this information properly; offering doctors additional supportive training, if appropriate. (And not forgetting that the doctors with the the worst recovery rates may be the ones predisposed to take the trickiest cases).
- carlmr 8y ago
  >(And not forgetting that the doctors with the the worst recovery rates may be the ones predisposed to take the trickiest cases). IIRC there are a lot of surgeons not taking on cases already because of the risk involved to their career. This might exacerbate it if the data is used blindly.
notahacker 8y ago
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 https://en.wikipedia.org/wiki/Harold_Shipman

coldseattle 8y ago

This will help with self-driving cars faced with a "trolley problem." You can choose the victim most likely to die anyway.

dvdhnt 8y ago
You forgot the /s flag.
kypro 8y ago
The trolley problem here already exists regardless. Just because making difficult decisions is uncomfortable doesn't mean the most moral outcome is to leave it to chance.

jand 8y ago

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?

newsbinator 8y ago
More data is good. It's better if this becomes an ethical/moral problem about when to care for patients and to what degree, rather than a guessing game problem while anybody's guess is as good as anybody else's.
dapearce 8y ago
>Customer A does not receive full medical support because "he's dying anyway"? Unless the patient consents to stop or reduce care, this would be medical negligence, which is illegal (in the US). Medical providers are required by law to provide medical treatment with all the knowledge and skill they possess.
- dragonwriter 8y ago
  > Medical providers are required by law to provide medical treatment with all the knowledge and skill they possess. No, to avoid liability for malpractice, they need to meet the “professional standard of care”, which is assessed by general practice in the medical community, not “all the knowledge and skill” the individual provider possesses.
  - deleted 8y ago
    [deleted]
- dekhn 8y ago
  This is not factually correct.
- Broken_Hippo 8y ago
  I wish that were the case. They'll treat you in the ER, but not make sure you can actually purchase antibiotics to cure the thing that brought you to the ER to begin with, nor make sure you can afford the bone specialist you need to see for your surgery. Anything short of an emergency and you can be denied care due to finances. Even if your problem is cancer or they know it will eventually be an emergency or life-threatening. Money is part of the reason folks will use the emergency room instead of urgent care: Urgent care requires upfront payment, the emergency room does not. Many doctors will refuse to see someone if they do not pay upfront. Luckily, different agencies tend to step in with end of life care.

hprotagonist 8y ago

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 solid 90% of AI is data munging, so that work isn't going away even a little bit; historically, Google throwing the kitchen sink into healthcare problems with lots of noisy data leads to flu seasons being correlated with softball victories or other wildly spurious correlations.

LyndsySimon 8y ago
> 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.
- deleted 8y ago
  [deleted]

baxtr 8y ago

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 hate that she died, but it’s even worse to know that all her data died with her and won’t help any other patient. I think this is one of the rare cases where collecting more data would help protect people

pavel_lishin 8y ago
I'm sorry to hear about your loss, but can you elaborate on what data was discarded after her passing?
- baxtr 8y ago
  Thank you. Well, the way I understand it: her treatment history won’t be considered by any other doctor than her own. The reason for that is that she wasn’t part of a proper medical study. However, I believe there is value in her data. The last days she was treated with an experimental drug. If there was an open database on all cases with treatment history I could have looked into that and get a feeling what to expect. These things often come with huge side effects, so you want to do it only if there is a slight chance. Also, if this data was collected globally, the number of reference cases would be much larger.
  - joshgel 8y ago
    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 significantly lesser quality than that collected as part of organized research studies. This is because in a research study, all patients get the same studies and tests whereas in clinical care, they only get what they need, so there is significant amounts of 'missing' data.

digitalzombie 8y ago

Why did they just use a logistic model instead of survival/time-to-event model? https://www.ncbi.nlm.nih.gov/pubmed/21478775 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 survival analysis, a whole branch that is focus on patient and their survival rate for the medical field. Google chose to compare to a paper and algorithm that focus on what seems like making hospital more money instead. I've seen a lot of data science people goes into different field and just telling people they can make money for them. It's great but with healthcare I don't think people should be treated as dollar signs. If anybody up and coming wants to use data science in bio, I would encourage them to look into statistic and biostatistic. We have tons of stuff already and then branch out to ML later. But at least know what's out there and there are establish organization, nonprofit out there too that all they do is biostat and build model. My friend works at a nonprofit child oncology. I just want to point out there are people that's building model to help patient with terrible sickness out there to survive. We're not diddling our thumbs trying to make other people richer.

louden 8y ago
> 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.
- toomuchtodo 8y ago
  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.
  - louden 8y ago
    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).

waydowntogo 8y ago

I'm programmer and I'm always saying (something like this) - the program / AI can be able to do things the best way how it can be done the best way by its programmer(s) My point is - this is completely nonsense. IT world and Real world are two difference worlds. It's look like Google needs wake up call.

ukj 8y ago
Which is why positive AND negative feedback loops from your users/customers/<people whose lives your software will affect> are vital to building great software.

neurobashing 8y ago

I'm sure it's an interesting technical challenge with huge amounts of complex, nuanced debate involved, and it could have a huge impact on health care. That said, my wife is an oncology nurse, and I will bet everything that no machine will ever get better at predicting than an experienced, skilled nurse. Humans are built to read humans.

adrianN 8y ago
"ever" is a pretty long time though.
evandijk70 8y ago
I would be very willing to take that bet on a 30 year horizon Just think about the machines/algorithms your wife already (indirectly) uses to predict outcome for patients. - An MRI scan, which is created from physical laws (Maxwell's law together with a quantum mechanical understanding of hydrogen atoms) and reconstructs a 3D-image of the different tumors. Knowing if and where metastasis are present have an enormous impact on the prognosis. - The algorithms used to align sequencing reads from biopsies to determine the mutation status, which are critical to determine the prognosis of some tumors. In fact, I'd wager that if your wife did not have access to these algorithms, she would already perform worse. And, in my opinion, the distinction between machine learning and the algorithms used to reconstruct 3D-images or align sequences is somewhat arbitrary.
mcny 8y ago
» my wife is an oncology nurse Have you ever asked her if she wants to do the job of predicting when a patient will die? How many hours would she need to spend reading the patient's history to come up with a prediction she feels satisfied to use to decide what service the patient will get? I'm not a medical professional but my gut reaction is I don't want to do anything with predicting or sentencing if some machine can do it almost as well as I can. I remember this story of a radiologist who told me he thinks we spend too much money for too little during the last about six months of a patient's life. If we had better information on when the last six months starts, maybe we could reduce the cost of healthcare? Apparently, we spend cost to 18% of GDP on healthcare in the US? Iirc, most of Europe is closer to 12?

b34r 8y ago

I’m sure insurance companies will be glad to get their hands on this to use it as an excuse to deny coverage.

Eridrus 8y ago
Insurance is all about pricing risk accurately, so yes, of course, these ideas will be used to price the risk more accurately. The problem isn't that insurers are horrible people, it's that insurance is a bad fit for healthcare.
- Spivak 8y ago
  Why do you say that? Take insurance in its most basic form: a company does its best to determine how much money they're likely going to pay out over the lifetime of a policy and spreads that cost out for their customer who is then insulated from unexpected expenses or non-uniform cost schedules. It seems perfectly suited to medicine. Insurance doesn't solve the problem of a population where the total cost of care is more then they can collectively afford or when an individual's treatment has known costs higher than what they can afford but nobody has a good answer for that. Insuring everyone from birth can be a solution for the individual but not the population.
  - Eridrus 8y ago
    Insurance works when you incur a lot of one time costs, it works much worse when you start incurring a lot of expenses over a long period of time. Ideally you could buy insurance that would cover all of your long term costs once you contracted some disease, but this doesn't exist in the US. And doubly doesn't exist when you realise that there are a bunch of forces incentivizing people to get their insurance from an employer.
    - Spivak 8y ago
      > you could buy insurance that would cover all of your long term costs once you contracted some disease Kind of defeats the point if you're using it to cover known future costs. Insurance can really only insulate you against risk, it's not free money.

codetrotter 8y ago

- “Hey Google! How long do I have left to live?” - “Are you sure you want to know?” - “...”

knuththetruth 8y ago

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-nurse-overdosed-clients-for-money/501-564776570 https://www.cbs19.tv/mobile/article/news/north-texas-hospice...

sireat 8y ago

How soon until Lifeline is a reality for a subset of patients? https://en.wikipedia.org/wiki/Life-Line https://en.wikipedia.org/wiki/Life-Line