EPIC IMPOC is a clinical decision support system used in the NHS to improve the management of infectious diseases by facilitating data collection, infection diagnostics and antimicrobial therapy advice at the point of care.

In the midst of the SARS-CoV-2 outbreak, we implemented a new module in EPIC IMPOC (Enhanced, Personalized and Integrated Care for Infection Management at the Point of Care), specifically to provide aid to clinicians to diagnose COVID-19 patients and treat them by retrieving similar cases.

During this project, I have preprocessed the data using Pandas library and selected the most important features using Random Forests.

I have also worked on the Probabilistic Inference module estimates the probability of COVID-19 infection in a patient based on key pathology data. I have tested different machine learning algorithm using mainly Scikit Learn library and the following algorithms were selected : LightGBM, Random Forest Classifier and Decision Tree Classifier.

Finally, I have implemented the Case Base Reasoning system which retrieves similar patients and provide them to the clinicians to support them with treatment prescription using, on one hand, the pathology profile, and on the other hand, the vital signs, symptoms, radiology results and medical history of the patient. This has been done by computing the weighted distance between a given patient and the remainder of cases and selecting the most similar cases. The system’s performance was evaluated using the patient’s outcome to verify and investigate patient similarities.

For both the Probabilistic Inference Module and the Case Base Reasoning system, different filters, imputers and scalers were tested and selected to maximize the performance.

• data processing
• machine learning