Machine Learning Predicting Hospital No-Shows using neural networks Approximately 20% of medical appointments that are booked are not attended in the UK. This costs an estimated £1 billion annually. If we were able to predict who might not show up, we
Want to join the team? At CodeMD, we're looking to bring together a team to work towards the common goal of empowering everyone to innovate for healthcare! We're looking for people with a special skill that you think
Welcome to CodeMD! After a slight delay, we've launched! What a journey it's been to get here - with lots of twists and turns along the way. We've learnt a lot about what sort of platform
FY1 How to make a Medical Calculator (SIRS) Systemic inflammatory response syndrome (SIRS) is a problem in hospitals. It refers to widespread inflammation across the body. Using a simple program, we can create a calculator to rapidly (and automatically) determine if
SHO Quantifying emotions in language using code! Quite often, the data that we have is in a written format. We may want to convert that to a number for analysis, and so may analyse it ourselves using a set of
Medical Student Using supercomputers to code for FREE! Here at CodeMD, we believe that learning to code shouldn't be a slow, laborious process. We want to keep this tutorial as short as we can to ensure you get enough information to
Join the Slack Community! We're delighted to announce that we've got a slack channel for everyone to join to meet other like-minded individuals, get help from the community, and work on projects together!Join the Slack channel
Registrar Data Science with Breast Cancer Data Pre-RequisitesEnsure that you read the Getting Started tutorial to learn the basics of Colaboratory and how to navigate around files, and the interface. We'll be using Colaboratory for our code so we won't
Consultant Machine Learning with Chest X-Rays Chest X-Rays are used all the time in hospitals. By using Machine Learning (ML), we can train an algorithm to triage our chest x-rays to highlight those with a high risk of pathology.
