Real time machine learning assisted emergency diagnosis

Hospital emergency departments are very busy places, with long wait times and a substantial proportion of patients misdiagnosed. This project aims to use machine-learning AI techniques to improve emergency department outcomes in the following three aspects:
1) By providing a list of plausible diagnoses for each patient, allowing the physician to check that they have not overlooked anything.
2) By suggesting informative tests for diagnosing a particular patient, reducing waiting times for patients and costs of unnecessary tests.
3) By linking real-time data from multiple hospitals, allowing identification of important trends and patterns in the data, allowing health services to react quickly to various events.
The project is to resolve a number of the challenges in building such a system.

Faculty Supervisor:

Toby Kenney

Student:

Partner:

Safeguard Intelligent Medical diagnosis Inc.

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

Dalhousie University

Program: