4 ms·

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/pubme

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).
    - deleted 8y ago
      [deleted]
corporateslaver 8y ago
Maybe they tried cox regression and it didn’t work. I’ve used that model and went with logistic regression before
Rainymood 8y ago
>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.
- Arnt 8y ago
  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.
  - kgwgk 8y ago
    > slightly impossible Nice way to put it.
  - LyndsySimon 8y ago
    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.
    - Arnt 8y ago
      There are many minor variations. Another common one is the variation where you you pick an insurance plan before your illness, and the insurer later decides on the details on behalf of all of its insured individuals. And... but they're all minor variations.
sidrajaram 8y ago
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.

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).
  - deleted 8y ago
    [deleted]

corporateslaver 8y ago

Maybe they tried cox regression and it didn’t work. I’ve used that model and went with logistic regression before

Rainymood 8y ago

>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.

Arnt 8y ago
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.
- kgwgk 8y ago
  > slightly impossible Nice way to put it.
- LyndsySimon 8y ago
  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.
  - Arnt 8y ago
    There are many minor variations. Another common one is the variation where you you pick an insurance plan before your illness, and the insurer later decides on the details on behalf of all of its insured individuals. And... but they're all minor variations.

sidrajaram 8y ago

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.