EVENTS

WiDS Datathon Maastricht 2021

The Institute of Data Science is hosting the Women in Data Science (WiDS) Datathon 2021 in Maastricht on the 5th and 6th of February!

Details

05 February 2021 - 09:30 -17:30

You will be given a dataset and a limited amount of time. You will be challenged to use your creativity and data science skills to build, test, and explore solutions. Try something new, apply what you know, learn from other participants and improve your data science skills along the way!

WiDS Datathon is open to individuals or teams of up to 4 participants, at least half of each team must be individuals that identify as women.

Everyone is welcome to participate in the WiDS Datathon: students, faculty, civil/public servants or anyone coming from the private sector. We encourage everyone to join regardless of their level of knowledge in data science!

This year’s challenge
The WiDS Datathon 2021 is a closer look at last year’s patient health data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative, with privacy certification from the Harvard Privacy Lab. This year, the aim is to determine whether a patient admitted to an Intensive Care Unit (ICU) has been diagnosed with a particular type of diabetes in the past.

Getting a rapid understanding of a patient’s overall health has been particularly important during the COVID-19 pandemic as healthcare workers around the world struggle with hospitals overloaded by patients in critical condition. In light of this, diabetes is a tricky condition as on a global scale 463 million adults were living with diabetes in 2019, and 1 in 2 (232 million) people were undiagnosed.

Participating teams need to create a model that predicts patient diabetes diagnosis history. Data analysis can be done using your preferred tools and submitted to Kaggle platform. The local winners will be determined by the leaderboard on the Kaggle platform at the time the Datathon closes on February 6th.

You can register here.

For more information please visit the Maastricht University webpage.