The inspiration comes from using real time data in a way that positively impacts others

What it does

Takes the inputs of

  1. L-CORE (patient's internal temperature in C): high (> 37), mid (>= 36 and <= 37), low (< 36)
  2. L-SURF (patient's surface temperature in C): high (> 36.5), mid (>= 36.5 and <= 35), low (< 35)
  3. L-O2 (oxygen saturation in %): excellent (>= 98), good (>= 90 and < 98), fair (>= 80 and < 90), poor (< 80)
  4. L-BP (last measurement of blood pressure): high (> 130/90), mid (<= 130/90 and >= 90/70), low (< 90/70)
  5. SURF-STBL (stability of patient's surface temperature): stable, mod-stable, unstable
  6. CORE-STBL (stability of patient's core temperature) stable, mod-stable, unstable
  7. BP-STBL (stability of patient's blood pressure) stable, mod-stable, unstable
  8. COMFORT (patient's perceived comfort at discharge, measured as an integer between 0 and 20)

and predicts the chance of

  1. decision ADM-DECS (discharge decision): I (patient sent to Intensive Care Unit), S (patient prepared to go home), A (patient sent to general hospital floor)

How I built it

It was built in python

Challenges I ran into

making my code acceptable and error free for the backend of the website

Accomplishments that I'm proud of

I made a real time machine learning model based on inputs passed from a node.js backend

What I learned

I learned how data science is processed on the backend of servers

What's next for PostOPDATA

Deploying to the cloud for a production environment

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