representing the OACTIVE project.
Invited Speakers Oral presentation, as well as from Poster Presentations can be found.
An interesting paper by OActive Project partner CERTH about the “Physical Activity as a Risk Factor in the Progression of Osteoarthritis: A Machine Learning Perspective” is now published in the conference proceedings of the “International Conference on Learning and Intelligent Optimization LION 2020: Learning and Intelligent Optimization”.
“Knee osteoarthritis (KOA) comes with a variety of symptoms’ intensity, frequency and pattern. Most of the current methods in KOA diagnosis are very expensive commonly measuring changes in joint morphology and function. So, it is very important to diagnose KOA early, which can be achieved with early identification of significant risk factors in clinical data. Our objective in this paper is to investigate the predictive capacity of physical activity measures as risk factors in the progression of KOA. In order to achieve this, a machine learning approach is proposed here for KOA prediction using features extracted from an accelerometer bracelet. Various ML models were explored for their suitability in implementing the learning task on different combinations of feature subsets. Results up to 74.5% were achieved indicating that physical activity measured by accelerometers may constitute an important risk factor for KOA progression prediction especially if it is combined with complementary data sources”.
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“In the past three decades, we have experienced an explosion of knowledge in all areas of medicine. The increasing use of “wearables” in everyday life in recent years, decreasing prices of biotechnical methods, and improved understanding of biomarkers and genetic sequencing methods, are creating unprecedented new opportunities, especially within rheumatology.
With the accelerating flow of scientific data, our knowledge grows rapidly, but also shows an immense growth in complexity”.
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ICH E6 (R2) requires sponsors to maintain responsibility for oversight of vendors, including those handling your clinical trial data. This often is challenging, particularly for companies with limited or no biometrics departments internally. Correct and effective data oversight increases your confidence in your clinical trial results and supports progress of development programs. During this webinar, we will review the components of biometrics oversight, assess various approaches, suggest an effective data oversight strategy and present case studies. The key learning outcomes will be:
• Understanding of the importance of oversight for clinical trial data
• How to choose the right approach to data oversight
• What are the fundamental components of an oversight program
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How healthcare stakeholders can win within evolving healthcare ecosystems.
“The healthcare ecosystems of the future will likely be defined by the needs of different patient populations and their associated effective care journeys (including beyond care itself). The consumer-oriented nature of these ecosystems also will increase the number of healthcare touchpoints, with the goal of modifying patient behavior and improving outcomes.
Healthcare ecosystems will emerge to address the needs of healthy patients, who have less consistent medical challenges, but often set personal wellness goals. These patients will likely experience a more digital ecosystem, where patient data and insights are consumed in a highly personalized and meaningful way, such as with wearable devices. Only a small percentage of the touchpoints would be in modalities of traditional care.”
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