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DiagnosEasy
Yourdiagnosis,in a heartbeat
Theidea
Create a tool that aims to assist medics in the
diagnosis of their patients.
Cool,buthow?
When Eurac data meets with medical
information about the health of the patient, we
are able to extract insights about the patient
health situation and even use statistical model
to calculate the likelihood of diseases.
TurnsOUTCalculatingriskishard
Lots of math and statistics is involved. Luckily
scientific literature comes in clutch and provides
a list of constants and variables to help us tackle
this problem.
Don’t get lost in the math. This is to
demonstrate that different parameters have a
different weight in determining the likelihood of
a particular disease.
In particular this is an example of the
Cambridge Diabetes Risk Score.
MATHs??MATHS!
weightedk-nearestneighbors
Knowing the parameters of the patient in
examination we are able to cluster patients with
similar clinical situation contained in the
provided dataset and estimate the likelihood of
disease by looking “around” our data point and
figuring out if similar patient have a pathology in
common.
let’sgettechnical
WebApp made with Ionic
Django backend
A bit of python to elaborate all the data as the
cherry on the top 🍒
Let’sdiveintoit!
🤿

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