Online event
On demand
Online event
On demand
Healthcare is an industry where the decision-making process is highly complex and possible mistakes might have critical consequences. Recently, AI systems appeared capable of quick comprehension of large sets of information that might empower medical and pharmaceutical specialists to draw valid conclusions, resulting in faster and more reliable decisions. Join us to learn more about the capabilities of such AI tools and discuss the future of healthcare.
Welcome speech by Olivier Jarry
A Guide to Big Data Analytics Tools in Technology Transfer by Kleanthis Mazarakis
15 min
Big analytics tools, like multivariate data analysis (MVDA) and design of experiments (DOE), can improve knowledge transfer capture to support technology transfer activities by making process information readily available to R&D, process development, commercial manufacturing, and other departments. In this presentation, Sartorius Data Analytics will present how SIMCA® and SIMCA®-online can be leveraged to optimize technology transfer activities across the product lifecycle.
How Sartorius uses machine learning to quantify biology by Rickard Sjögren, PhD
15 min
Due to converging trends in automation, digitization, and artificial intelligence, the biopharmaceutical industry is currently being transformed. As a consequence of this transformation, vast amounts of data are generated all the way from early molecule discovery to commercial manufacturing. This offers a great opportunity to leverage data analytics and machine learning, understand the complicated nature of both underlying biology and manufacturing processes, and provide guidance on how to improve them. In this presentation, we show how state-of-the-art machine learning holds great promise to automate and augment biological interpretation of microscopic imaging data, which is widely used to better understand diseases such as cancer and Covid. We hope to provide a part technical, part inspirational example of how machine learning helps bring better health to more people.
Applications of NLP for Clinical Data by Marcin Mosiołek
15 min
The digitalization of the health industry has resulted in the collection of massive amounts of clinical data and the creation of electronic health records (EHRs). Efficient use of such data is highly challenging due to its complexity and heterogeneity, but it is undoubtedly worth the effort as it contains a tremendous amount of knowledge. Luckily, we live in the era of Artificial Intelligence. Thus, one might apply state-of-the-art Natural Language Processing techniques to build solutions outperforming humans-experts while dealing with vast amounts of highly complex documents. In this webinar, I'd like to discuss the possibilities of modern machine learning methods for clinical documents processing, including diagnosis support, drug-drug interaction identification, medical concepts extraction, telemedicine, and the interpretability of such methods.
Predictive Site Selection (PSS) by Vladimir Tsutskhvashvili
15 min
Country and site selection, particularly within a highly competitive indication, can significantly affect development costs and timelines. While the use of historic recruitment data can determine selection strategy, the performance of a site does not necessarily correlate with its previous recruitment success. Achieve a new level of performance by linking internal and external data sets to build a predictive machine learning model with a higher degree of precision in predicting drivers of site performance (e.g. site congestion, protocol parameters). Applying such algorithms has enabled us to identify the ideal sites and the attainment of recruitment ambitions with fewer activated sites.
Q&A Session moderated by Olivier Jarry
Kleanthis joined Sartorius in September 2019, as a Data Scientist in the division of Data Analytics, based in London UK, and since then he has been helping customers from the Pharma Industry analyze their data and make informed decisions. Before joining Sartorius, he worked as a Data Scientist for OSIsoft, where he had the opportunity to assist customers from various industries in their Digital Transformation journey. He holds a bachelor’s degree in Chemical Engineering, as well as a Master's degree in Environmental Engineering from Imperial College, London.
Rickard has a background MSc in bioinformatics for biotechnology engineering from Umeå University, Sweden, where his thesis was about co-expression network analysis of omics-data. After his MSc, he worked as a software developer in a research group at Umeå University for two years developing data visualization software for biological data analysis. He then did his Ph.D. in computational science in the same research group where he studied ways to combine machine and deep learning with chemometrics. Rickard joined Sartorius in 2018, working as a machine learning research scientist in the Advanced Data Analytics team of Sartorius Corporate Research. He is now leading a small research team in Sartorius Corporate Research that uses modern machine learning to develop new product concepts and provide new capabilities to Sartorius instruments.
Marcin is an AI Architect with over ten years of experience collected in various machine learning projects, including natural language processing and computer vision. In his daily job, he converts the latest academic research into operating products and leads teams of AI experts. Marcin is currently working on an unstructured documents understanding and semantic search engine for the polish language. Both projects touch multiple areas of NLP, such as coreference resolution, unsupervised learning for sentence embeddings, document deduplication, etc. Previously, he built AI solutions for medical documents verification and many computer vision projects, such as the perception module of an autonomous car, including object detection, visual object tracking, etc.
For the past 5 years, Vlad has been leading the strategic initiatives in the Medical Affairs space, helping to make a shift towards a patient-centric mentality by applying ML and predictive analytics to improve productivity, efficiency, and patient experience in Clinical Trial and Patient Safety areas. Vlad has a Bachelor's degree in Integrative Biology from UC Berkeley and a Master's from the University of San Francisco. He spent close to 2 decades in the financial industry and was part of the customer-centric transformation of the industry, the experience he is able to apply in the Big Pharma industry.
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