Open Innovation has become a widespread way to boost R&D in our search for the solutions of the future. This process is found across industries, problems, and technologies; it is very visible in company websites, challenges, conferences, and professional media posts. Breaking organizational boundaries leads to many benefits, but it also comes with multiple challenges to overcome. Built on our experience with R&D and open innovation projects at PickleTech, we describe here some patterns we believe are key to reaching impactful solutions with Open Innovation.
The diagnosis and care processes for brain conditions such as depression or dementia are complex, involve multiple tests, data types and specialists; and they are, in some cases, very dependent on the subjective ability of the patient to report on their condition. Some of these challenges have become at the same time an opportunity to improve the healthcare processes by leveraging AI.
Multimodal Machine Learning solutions can assist specialists and provide them with new tools and insights to make the healthcare pathway more efficient and objective. In this post, we introduce one of the projects we are developing at PickleTech focused on such improvements: HALO. Starting from a cohort of elite athletes, we leverage multimodal AI and the development of biomarkers, aiming to provide athletes and preparation teams with objective measures of their mental health.
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.