Team: 70
School: Taos High
Area of Science: Biomedical
Interim: Team Number: 70
School Name: Taos High School
Area of Science: Biomedical
Project Title: Doctor’s Assistant Program (DAP)
Project Definition:
Misdiagnosis from medical professionals is a serious issue. The issue leads to mistreatment of patients which can sometimes worsen the problem. Misdiagnosis rates have been as high as 50% in recent decades, making it a relevant issue that should be addressed. Computer systems can be used to combat the issue by giving advice and can look over specific details that medical professionals may not consider. By using machine learning the program can learn from its mistakes and continually expand its medical database, making it a great candidate for this task.
Problem Solution:
To make the Doctor’s Assistant Program plausible the program will have to use servers to globally access its database and store patient sensitive information. Said patient information will be used to help diagnose patients properly based on past medical history. The program will still consider alternatives to a patient’s affliction but will have a bias towards the patient’s medical history. All relevant and crucial information will be presented to the medical professional seeing the patient. The medical professional will then diagnose the patient, not the program.
Progress to Date:
Due to the addition of a server the team decided to rewrite the program entirely, though it remains in Python. The team has taken a slightly aged desktop and loaded ubuntu on it. The server is currently not functional, but the team has made good progress in creating it. The team is also working on a GUI for DAP. Using tkinter the team has made a basic GUI which will soon be accompanied by many webhooks and become integrated with the server and program functions.
Expected Results:
Once the server and databases have been completed the program should be fully functional with GUI and all dependencies. The program should be able to build and retain patient files, and additionally reference them when advising a medical professional’s diagnosis.
Team Members: Haven Hennelly, Ryan Cox, Sawyer Solfest, Skylar Spriggs
Sponsoring Teacher: Tracy Galligan
Team Members:
Sawyer Solfest
James Ryan Cox
Skylar Spriggs
Haven Hennelly
Sponsoring Teacher: Tracy Galligan