Real Medical Data Hackathon with AUMC and Deloitte
The 24-hour Medical Data Hackathon organised by Deloitte and Amsterdam University Medical Hospital (Amsterdam UMC) saw data scientists working with AI students on real data from intensive care. In the process, the teams came up with real world solutions.
On 5 and 6 March 2020, the 24-hour Medical Data Hackathon took place at De Nieuwe Poort in Amsterdam’s business centre Zuidas. The event brought together data scientists from Deloitte with students following a masters in artificial intelligence at VU Amsterdam.
For 24 hours, the teams worked with machine learning models using ICU data from the Amsterdam UMC – who recently made headlines as the first hospital in Europe to make data on ICU patients available for research. Doctors from the UMC were also on hand to ensure the questions were as clinically relevant as possible.
The challenge: improving care for ICU patients
Amsterdam UMC collects billions of data points from their ICU patients and now hopes to discover patterns in this vast amount of data to help improve the treatment of patients and reduce the number of complications. To focus the hackathon teams, UMC doctors formulated three challenges faced by intensivists:
- Can we predict atrial fibrillation in the intensive care unit ahead of its onset?
- Should we treat an increase of lactate for a specific patient in the intensive care unit?
- Can we predict whether we can stop antibiotic treatment in a specific patient in the intensive care unit?
The winners: complete with app dashboard
After a long tallying process, a winner was announced: The Beta Blockers. They had taken on the third challenge: how to help doctors determine the best moment to stop antibiotics – in the name of saving money and staving off microbial resistance – while not sacrificing on patient outcomes.
Not only did The Beta Blockers get promising results, they were already able to translate their results to a clinically useful application for doctors – complete with a dashboard that showed which patients could have their antibiotics reduced or stopped without influencing outcome.