Particle accelerators have a direct impact on society through several of their applications in medicine. With the advancement of accelerator science, new techniques for the treatment of cancer and diagnosis of various diseases have provided major steps forward in healthcare during the last decades. But particle accelerators are hi-tech machines with a challenging operation. In this post, we describe how Data Science solutions help automating their operation and optimizing the performance of such fascinating facilities.
Medical devices containing a machine learning algorithmic component, e.g. for diagnosis, have a huge market and health potential. But getting the approval to sell a medical device is possible only after a thorough process of certification. For this, the particularities of statistical learning need to be taken into account as soon as possible in the device design. Otherwise, the risk of getting trapped in the certification process with suboptimal -viability killing- components is just too high. In this post we describe good practices for machine learning development and operationalization within medical devices, driven from our experience at PickleTech.
In Emergency Medicine, decisions need to be made fast, and with a limited view on the patient’s health status. While the time urgency is imperative, in Emergency Medicine not all the decision outcomes are visible immediately. Actually, some may take quite some time to show up, turning feedback and learning processes into a challenge. Limited resources impose in addition a very severe constraint, which makes Emergency Medicine too many times a complex resource optimization problem in nature. We discuss here how Data Science has the potential to improve Emergency Medicine, focusing on some of the opportunities for such a complex context.
At PickleTech we thrill when two -apparently- separate projects end up connected in some way. And when the reason is related to Physics, writing about it becomes an imperative. Do you know what connects a Tour de France stage with the management of a constellation of satellites?
Aerodynamic drag heavily impacts both cycling and low Earth orbit satellites. Estimating drag with precision and integrating it on complex operational strategies are key to their success. Data Science is contributing to optimize such complex challenges with the use of Artificial Intelligence and Machine Learning techniques.
Gradient Adjusted Pace (GAP) models are integrated as a common feature in many running sports devices and companion apps. Recently, Kilian Jornet announced his movement from Suunto to Coros, and one of the anecdotal quotes to motivate the change was Coros interest on Kilian’s adjusted pace model. How useful is it to look at a GAP model not derived from your own individual data?
Kilian-Coros’ example highlights the importance of individualized performance modeling for athletes. In this post, we derive the individual GAP model for Kilian. Thanks to his collaboration, we have computed the model using only his own recorded data from last year’s activities. We compare his individualized GAP version with the public version by Strava, focusing on what makes Kilian the ultimate mountain athlete.