The heart is one of the most important organ in the human body as it is repsonsible for pumping blood to ohter organs in the body. When a person's heart stops, the other organs in the body will slowly cease to function when it runs out of blood, a phenomenon known as a cardiac arrest.
When a person enters a cardiac arrest state, they will be taken to the hospital to be resuscitated. For most resuscitated patient, they enter a coma state after being resuscitated. These coma patients will also require life support machine to keep them alive as they are unable to support themselves in a coma state.
Due to the limited amount of resources, doctors will sometimes remove the life support from the patient if their prognosis does not show promising recovery, which raises a concern if any of these patient who had their life support machine removed could have recovered. Additionally, the process of generating these prognosis is also very labour intensive and time consuming.
Therefore, to reduce the labour and time required for generating the prognosis and to also generate a more acurate prognosis, a deep learning model can be used to achieve these goals.
To develop a Convolutional Neural Network (CNN) Deep Learning model that is capable of predicting the neurological recovery of a coma patient at a high degree of accuracy.
- Download and Install Docker Desktop (https://www.docker.com/products/docker-desktop/)
- For ARM64 processors (Mac M1, M2 etc.), run the following command
docker compose -f compose_bulid_arm.yaml up
For x86 processors, run the following command instead
docker compose -f compose_build_amd.yaml up
- Once the frontend, backend and database server is fully loaded, access http://localhost:3000 through your browser
| Technology | Purpose |
|---|---|
| PyTorch | Deep Learning (CNN) |
| scikit-learn | Machine Learning (SVM) |
| PostgreSQL | Web Application Database |
| Django | Backend Web Application |
| Next.js | Frontend Web Application |
| Docker | Containerization |
- Jensen Kau ([email protected])
- Quek Kai Xen ([email protected])
- Teoh You Xian ([email protected])
- Dr. Fuad Noman ([email protected])