Deep supervised learning model to classify risk of death in COVID19 patients based on clinical data

This project involves taking a dataset that consists of clinical data (26 unique features) of patients that have been diagnosed as COVID19 positive and creating a deep learning model in using PyTorch to classify a patient’s risk of death and need for hospital admission based on their clinical features.

There is a two deliverables in this project with milestones:
1) $20 plan and execution of data imputation, regularization and linear regression if needed
2) $230 completion of the deep learning model in PyTorch with the ability to classify the input data at >90% accuracy

If you have experience coding a front-end in flask, there will be additional compensation for you to do this after the completion of the deep learning model. If you can integrate and make use of IBM’s quantum computing platform using Qiskit to introduce a trainable quantum node, you will be paid an additional $100.

Please ask me for the raw data when submitting a proposal.