Improving Cardiac Arrest Prediction Using Machine Learning

In this 8-weeks challenge, 50 AI changemakers from all over the world collaborate to build an AI healthcare solution to predict cardiac arrest due to pulseless electrical activity and asystole using Machine Learning.

This challenge requires experience in Data Engineering, Machine Learning, and Predictive Modeling.

The Problem

SCA is a medical emergency in which the heart suddenly stops beating, killing the patient within minutes. Survival rates for SCA are <25% within hospitals. SCA can be prevented if the underlying cause is identified and treated. When SCA occurs, immediate electric shock to the heart improves chances of survival, but delays of just two minutes between SCA onset and shock lower survival rates and raise rates of brain damage among survivors. No technology on the market alerts clinicians of a patient’s risk of SCA. Current cardiac monitoring machines only issue alerts after a patient experiences an arrest, forcing healthcare providers to race against time.

The Project Goals

The purpose of this project is to expand the cardiac arrest prediction algorithm to pulseless electrical activity and asystole, providing an all-cause cardiac arrest prediction algorithm for more than 90% of patients. The work will include the following: 

• Carry out exploratory data analysis: visualization, case-control proportion, choice of  methods, and metrics 
• Perform data cleaning: noise, NaNs and Infs, missing data 
• Separate the data into folds 
• Feature engineering and selection 
• Model building and evaluation

Useful Resources

Transformative has developed CodeRhythm™ with a standard data science stack using python, pandas,  NumPy, Numba, TensorFlow, Sci-kit learn, SciPy, and Keras. They perform preprocessing of physiological data and store algorithm states throughout data batches. 

The data are largely sourced from https://mimic.physionet.org/

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.