Clinical AI Conference & Datathon aims to foster collaboration between physicians and data scientists to improve patient care. Participants will deepen their understanding and skills of the extraction of clinically relevant knowledge form data during the conference keynote talks and hands-on workshops. They further have the opportunity to work on a challenging case study during the datathon running in parallel to the conference.
Mankind has always been fascinated by the idea of creating technology that would match or even supersede human intelligence. While artificial intelligence after its birth in Dartmouth in 1956 had a rather slow start in medicine, new computational technologies and data acquisition, storage and analytic methods have arisen to allow the utilization of knowledge from millions of clinicians encoded in large clinical repositories. However, extracting this knowledge and translating it into clinical practice is a complex task, strewn with pitfalls. To identify and overcome these obstacles, international community efforts are required, combining the strengths of the knowledge of clinicians and data scientists. Therefore it is our mission to bring people together, combine strengths of these individual disciplines to achieve better patient care.
What is a Datathon
A Datathon in medicine is a format, where interdisciplinary teams work with a database or dataset to answer medically relevant questions. In our Datathon, we will provide a dataset extracted from the MIMIC III Intensive Care database.
This format has recently gained great attention in the medical domain. Its interactive nature brings together various groups, such as medical professionals, data scientists, informaticians, epidemiologists, and healthcare industrialists. Hence, it enables highly efficient utilization of the enormous amounts of health data available these days.
Different teams will work on the same dataset and results will be compared at the end of the conference. This is a great chance for medical professionals to get involved with and learn about methods and possibilities of statistics and machine learning, and for informatics, statisticians, and epidemiologists to learn about medicine.
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