
How AI changed organ donation in the US - rbanffy
https://qz.com/1383083/how-ai-changed-organ-donation-in-the-us/
======
robbiep
As a medical student I sat through assessments of liver failure patients to
assess their suitability for transport. There were some particularly
heartbreaking moments there, such as when a patients wife seperately said to
us that she didn’t believe he was suitable because he would just waste it.

Another horrible example was a patient with fulminant liver failure secondary
to Hep C. Most people in Australia contract Hep C through IV Drugs or sexual
activity, but during the 80s people contracted it through contaminated blood.
This patient had been at the top of the transplant list 3 times but because he
developed spontaneous Bacterial Peritonitis each time he was ineligible and
dropped back to the bottom. I believe he died not long after I took his
history. I’m so pleased we have free treatment for Hep C patients in Australia
now.

Organ donation is a heartbreaking issue and the sample conversations of the
God Committee allocating access to the first dialysis treatment is revealing
(even though they’re discussing access to treatment and not transplant)

For those wondering, as I didn’t see it mentioned in the article, dialysis
treatment costs approx $20-30k AUD a year (at least in Australia). Average
survival is 10 years from initiation of treatment.

A kidney transplant costs $30-40k. In Australia there are access schemes
whereby patients who may not otherwise qualify (ie obesity) have been given
gastric sleeve procedures by the healthcare system in order to be eligible for
a transplant, on the economic basis that $200k in dialysis is more than the
$50k of an elective surgery and then transplant. Usually in these situations
the donor is a family member.

The use of matching algorithms as described in this article is so valuable in
terms of lives saved, but ultimately no substitute for what we need: the
ability to grow new organs.

~~~
piyh
First organ transplant was 64 years ago. I hope in another 64 years we look
back on our current system like we do leeches now.

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hangonhn
Freakonomics has a really good podcast episode on chain donation

[http://freakonomics.com/podcast/make-me-a-match-a-new-
freako...](http://freakonomics.com/podcast/make-me-a-match-a-new-freakonomics-
radio-episode/)

But to call it an triumph of AI is stealing credit from the economists. It was
an economist who came up with the idea and the field of study, market design,
is fascinating, especially to those who are into startups and disruption.

~~~
majos
I had a similar reaction, since it's not like these matching algorithms do any
"learning" (as far as I know). And you are right that the credit here goes
largely to economists.

But the article does point out that we do a lot of goalpost-shifting when it
comes to defining "AI", so "algorithm solving what we thought was a very human
task" plausibly falls under that umbrella. I _think_ that's the rhetorical
point of using that word.

Or maybe they just figured "AI" drives clicks.

~~~
YeGoblynQueenne
>> I had a similar reaction, since it's not like these matching algorithms do
any "learning" (as far as I know).

Learning is not a criterion for AI/ not AI. The kind of system described
(though only very vaguely) in the article is a rule-based system, possibly an
expert system. These hand-crafted systems were the norm in AI up until the
'90s or so and are still in widespread use in various domains (e.g. in
finances, or air travel bookings etc).

In fact, machine learning took off largely as an effort to avoid having to
hand-craft rulebases and elicit expert knowledge from domain experts. You can
see this very clearly in decision-tree learners: these basically learn the
rules that a human would be creating by hand.

It's also possible that the system described in the article is using some kind
of resource allocation algorithm, like a linear programming algorithm, or some
such. Those are actually optimisation algorithms, so they are easier to
recognise as AI by today's standards (i.e. by the standards of the last 5
years or so of press releases by Google :).

In any case- the majority of AI, historically, has nothing to do with
(machine) learning and instead focuses on reasoning in complex domains.

~~~
peoplewrong
> The kind of system described (though only very vaguely) in the article is a
> rule-based system, possibly an expert system. These hand-crafted systems
> were the norm in AI up until the '90s or so and are still in widespread use
> in various domains (e.g. in finances, or air travel bookings etc).

this is wrong at least for UNOS (united network for organ sharing)-which is
the main system in the article-the current computer program used to find
matches is an integer linear program. I think it is an interesting question if
some kind of rule based system could replicate their ability to find the
matches. I tend to think not.

[https://pdfs.semanticscholar.org/0837/80a157a7f8ebfa44dce0bc...](https://pdfs.semanticscholar.org/0837/80a157a7f8ebfa44dce0bc1e3e5035a5cdee.pdf)

page 16 "To our knowledge, there is no solver that would scale to the
nationwide steady-state size— including the CMU solver used by UNOS. This
solver is based on the work of Abraham et al. (2007), with enhancements and
generalizations by Dickerson and Sandholm, and uses integer linear programming
(IP) with one decision variable for each cycle no longer than L (in practice,
L = 3) and constraints that state that accepted cycles are vertex disjoint.
With specialized branch-andprice IP solving techniques, Abraham et al. (2007)
were able to solve the (3-cycle, no chains, deterministic) problem at the
projected steady-state nationwide scale of 10,000 patients"

edit: wait wait this comment could be out of date. The google scholar for this
paper says 2018 but the paper says 2013??

~~~
YeGoblynQueenne
Cheers. Just as you were posting this I read your comment and noticed, in the
link, the reference to MIP. However, it does sound like early matching systems
where simply hand-crafted rule-bases encoding experts' decision-making
process, so expert systems:

 _One night in 2000, tired of delivering the heartbreaking news to patients
and their loved ones that no suitable kidney could be found, a US nephrologist
named Michael Rees lugged home several crates of files and spent the next few
hours scrutinizing blood, antibody, and tissue data, and comparing patient
charts. The work was mentally grueling. Eventually, he realized he had no
viable matches—but also, that if the pool were bigger, pairs could be made.
Working with his father Alan Rees, a computer scientist, Michael Rees created
a simple computer program that did the work of pairing up donors and
recipients, introducing AI to the matching process._

~~~
peoplewrong
I bet the first version of this was some kind of maximum bipartite matching.
like hopcroft-kraft

but on the topic of experts check out this paper by the same research group.
This is much more of an 'AI' paper that does include expert domain knowledge
and learning

"FutureMatch: Combining Human Value Judgments and Machine Learning to Match in
Dynamic Environments" from AAI2015

[http://jpdickerson.com/pubs.html](http://jpdickerson.com/pubs.html)

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rexreed
like other commenters, I fail to see the AI here. How are matching algorithms
absent any machine learning considered to be AI? I thought AI was all about
solving probabilistic problems where deterministic ones didn't exist?

~~~
YeGoblynQueenne
Out of curiousity, how did you come by this definition? I can't say I
recognise it!

Like I say in another comment, most of AI is not about machine learning, but
rather about reasoning in complex domains, very often using hand-crafted
procedures.

For example, most of the AI systems of the '80s and '90s, which were in fact
hailed as great successes of AI, were of the expert system variety: in short,
a large, hand-crafted database of production rules elicited from domain
experts, coupled with an inference procedure.

For instance, an early expert system was MYCIN, used to diagnose bacterial
infections. It was one of the earliest AI systems to perform at least as good,
and even better than, experts:

[https://en.wikipedia.org/wiki/Mycin](https://en.wikipedia.org/wiki/Mycin)

~~~
rexreed
The sentence you're referring to is not a definition of AI, but rather it's
most suitable intent. If you have truly deterministic systems, you don't need
any sort of intelligence, other than the intelligence of the human that put
together the system. The machine is simply executing the intelligence of the
human and not providing any of its own or adding to the picture, other than
providing leverage by means of automation. Waffle irons aren't intelligent,
even tho they automate the process of making waffles. If your definition of
intelligence renders the machine to be simply an executor of intelligence from
humans, then that's not particularly useful and then you might as well just
say "computing" whenever you say AI. I tend to believe that AI is best suited
to solve problems that can't be easily solved with deterministic, programming
approaches. Machines can be trained to provide value on the logic and
decision-making side rather than just on the decision-executing side.

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mhb
AI???

