Many Machine learning applications are analogous to "experience'
ie pattern recognition
Typical ML algorithms require training data where the results are
known . They seem to have most application in areas where there are
massive amounts of data which a human cannot comprehend - eg facial
recognition in a crowd
Clinical problems on a one to one basis are a different problem -
encoding the symptoms/signs will be an issue
Interesting idea though
Doctors are not too good at sticking to known protocols where the
condition is known - machines might do better here
R
----- Original Message -----
From: "For openEHR clinical discussions"
To:
Cc:
Sent:Mon, 25 Jun 2018 13:02:42 +0200
Subject:Re: Machine Learning , some thoughts
On Mon, Jun 25, 2018 at 12:52:07PM +0200, Bert Verhees wrote:
> Allthough, there are some patient-conditions which are very typical
for a
> disease, mostly this is not the case.
> For example, many infection-diseases have fever as a symptom, and
one person
> gets pain in his back, and the other has headache as a result of
fever and
> other inconveniences coming with infection disease.
>
> So, the GP cannot do much with machine learning, the best source of
> knowledge is his experience,
Experience is, at most, an equal source to evidence. It
becomes "better" over time.
> and if he cannot solve with that, he should ask
> someone else, or send the patient to the hospital to a specialist.
Nonetheless, an algorithm _can_ scan records in the
background looking for telltale constellations indicating the
"I am sure" group (which we sometimes DO miss) and highlight
those to the GP.
Karsten
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