Inspiration
The inefficient system of medical processes in Ontario (long wait times, wacky prioritization, side effects of free health care).
What it does
Lets medical officials find patient history and includes direct visits through an app rather than requiring multiple steps to receive data. This system also encourages self diagnosis before visiting doctor to increase overall efficiency of the medical system of Ontario. The patient can start by self diagnosing (run thru machine learning) themselves through our website. Then they can go to their family doctor with a clear idea as to what the symptoms may be. This will allow doctors to quickly get an idea of the patients situation and the doctors themselves can upload information to the database and do necessary scans/checks on the patient to confirm this information. The doctor will input variables such as age, symptoms, initial diagnosis (thru ML), and final diagnosis (thru doctor). This data will be used to retrain the current model (information acquired from online database) with more detailed and present data. This data will also be personalized as patients all have their own ML diagnosis as well as a doctor's diagnosis.
How I built it
Challenges I ran into
-problems with machine learning -problems making the http api
Accomplishments that I'm proud of
-we got the machine learning working -the http api is easier to use than ever -the ui of the app and website are slick
What I learned
-machine learning is hard
What's next for MAIA
-pushing it out to doctors after adding age and gender specifications
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