Background
Addressing healthcare inequity is critical and has an extensive positive impact on health, society, and the economy. Women often encounter distinct health challenges and stand to gain significantly from efforts to ensure equity in access to healthcare. Achieving health equity involves addressing disparate treatments based on demographic and societal factors so that in the future women can achieve their highest level of health and well-being.

Dataset
It is a rich, real-world dataset which contains information about demographics, diagnosis and treatment options, and insurance provided about patients who were diagnosed with breast cancer from 2015-2018. The dataset originated from Health Verity, one of the largest healthcare data ecosystems in the US. It was enriched with third party geo-demographic data to provide views into the socio economic aspects that may contribute to health equity.

Your challenge
...is to predict the time of treatment of women patients who are diagnosed with metastatic triple negative breast cancers (metastatic TNBC). Metastatic TNBC is considered the most aggressive TNBC, and women who are diagnosed with it are among those who are in the most urgent need for timely treatment. You are expected to develop a model to predict how many days it takes for a patient to receive the first treatment for their cancer diagnosis based on patients’ characteristics. Differences in the wait time to get treatment is a good proxy for disparities in healthcare access. You are also encouraged to go beyond the predictive challenge to generate additional insights and to understand the real-world implications of the results.

Both the data challenge and the competition are hosted on the Kaggle Platform.

If you have not participated in Kaggle competitions before, or if you do not have sufficient data science skills, you can join our pre-training sessions to help you to use Kaggle platform, understand the dataset, introduce data analysis pipeline, brainstorming on possible solutions, etc.  The pre-training sessions will take place on Friday 2 February from 10:00-11:00 and on Saturday 3 February from 09:30-10:30.

As a warm-up for the Datathon, people who have experience in data science are also very welcome to join: Help others and get to know your team members for the Datathon!

Meet the Tutors