Amsterdam Medical Data Science

ONE THOUSAND MEMBERS AND COUNTING (!) Everything medical data science. And pizza. This edition features: ** Deep learning for Tumor Response Evaluation Geert Kazemier (Surgery, Amsterdam UMC) ** Why you should use our Research Infrastructure Arno Sinjewel (ICT, Amsterdam UMC) followed by discussion and networking with pizza

Organised by Pacmed in collaboration with Freedomlab. www.pacmed.ai www.freedomlab.org First speaker: David Ruhe (Pacmed, MSc Data Science) Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications. This talk will illustrate how the predictive uncertainty that Bayesian models provide can be used to mitigate risk of misguided prediction and to detect out-of-domain examples in a medical setting. Using the MIMIC-III critical care dataset, a Bayesian Neural Network was trained to predict mortality after discharge from the Intensive Care Unit. Empirical results show that such approach can indeed be used in practice to prevent potential errors and reliably identify observations for which the prediction is uncertain. David Ruhe researches methods to assess uncertainty of machine learning predictions. Specifically, he is looking at modeling uncertainty with Bayesian Neural Networks applied in Pacmed's Intensive Care Unit clinical decision support system. Second speaker: O’Jay Medina (UMC Utrecht) Rheumatoid arthritis (RA) is an autoimmune disease causing inflammations in joints during periods of high disease activity (flares). No permanent cure is available and treating with biologicals is both expensive and affecting the quality of life in patients. Empirical evidence shows that flares can be predicted based on patient data available in electronic health record systems by using data science and machine learning techniques. Using this evidence, a dynamic prediction tool is being developed to improve clinical decision-making in RA treatment. O’Jay Medina is a Data Scientist at the University Medical Center Utrecht (UMCU). He has a background in neuroscience and is currently part of the Applied Data Analytics in Medicine (ADAM) program. Apart from developing software to predict Rheumatoid Arthritis flares, he is a member of the UrStatus project, which aims to provide an intuitive visualisation of the status of patients in the Neonatal Intensive Care Unit (NICU) by using predictive modelling.

--- PLEASE READ CAREFULLY BEFORE JOINING THE WAITING LIST --- Are you a data scientist? And do you want help cure the sickest patients using your talents? Then join the AMDS Data Dream Team! We are continuously looking for the most talented data scientist to join our exclusive data dream team. These work directly with senior medical specialists, researchers and collaborating startups on our large intensive care database. This contains data from about[masked] patients that contain abundant time series, imaging and natural language data, ready new model development. In addition, our intensive care is very experienced in bringing models to the actual bedside of the patient. This means that these model are actually being used for the benefit of these critically ill patients in real time. Joining our team will give you the opportunity to truly contribute to curing real patients. As a bonus, this position offers unique learning and networking opportunities and will certainly boost you resume. Do you have what it takes? And are you willing to spend an average of four hours per week or more at Amsterdam UMC? Then please join our waiting list and we will contact you for the selection process.