Data-Driven Business Models In Pharma

ImprintFTWK GmbH & Co. KGGürtelstraße 2510247 BerlinTel: +49 30 667 637 50Fax: +49 30 667 637 53Handelsregisternr.: HRA 50013 BAmtsgericht: Berlin-CharlottenburgGeschäftsführer: Herr Lutz HaaseUSt-IdNr. DE296648743hello@ftwk.iohttps://ftwk.io

Lutz Haase

This is an exclusive event for a maximum of 10 guests. 
The pharma industry is beginning to acknowledge a crisis - despite increasing monetary efforts towards drug development, the number of newly licensed therapies stagnates. In result, the return of investment (ROI) dramatically decreases, bringing pharmaceutical development to the point of unprofitability (Tufts Institute, 2014). 
Many claim to have already found solutions to the crisis: digital health, artificial intelligence (AI), machine learning, big data, personalized medicine... In reality, these solutions are both a blessing and a curse. For few, these terms are more than mere buzzwords that fell short of expectations, only a handful are successfully using the innovation behind them. In order to get beyond the hype and best benefit from revolutionary technologies, decision makers need to have a thorough first-hand understanding of the meaning of these terms, what strategies they enable and what remains science fiction. In the course of the roundtable we will enable a deep understanding of data science in a hands-on way.
Participants will understand the exact concepts behind the technology without being bored with math and science. After the potentials of digital health, AI, machine learning, big data and personalized medicine become clear, we will see that existing strategies must be challenged - 21st century technology requires 21st century business cases. This will be the focus of the remainder of the session with existing real-world business cases, as well as outlining your future projects.
The aim of the roundtable is to provide an orientation guide for executives to help them navigate the AI/data science jungle and to identify new data-driven business models in healthcare.
 
About the organizer: <A HREF="https://www.linkedin.com/in/lutzhaase/" TARGET="_blank" TITLE="LinkedIn" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Lutz Haase, MBA</A> 
Lutz has more than a decade of digital industry experience, consulting Fortune 500 corporates on digital transformation. He has run several innovation initiatives, hackathons, design thinking workshops and design sprints to deliver innovative digital prototypes, products, and services for brands like AOK, Bayer, Biotronik, Boehringer Ingelheim, Roche Diagnostics or Thermo Fisher Scientific.
He holds an MBA and is a well-connected mentor in the Berlin start-up ecosystem. As an ambitious leader, he combines a strong entrepreneurial drive and a creative mindset, while he feels home at the intersection of user experience (UX) design, business model innovation and technology in digital health.
 
<A HREF="https://www.linkedin.com/in/joern-klinger-87a56465/" TARGET="_blank" TITLE="LinkedIn" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Dr. Jörn Klinger</A> 
Jörn is a data scientist with a background in linguistics, psychology and computer science. He holds a Master of Science at the University of Oxford, where he worked on what is now called deep learning and delivered the best dissertation of his cohort. He went on to pursue a PhD at the University of Texas at Austin, where he studied social learning from three different perspectives: He did field experiments in an indigenous community in Mexico three months a year and then corroborated his findings by building computational models and using big data from social media.
After his PhD Jörn firstly joined the management of a big data startup in London. Believing in his idea to apply his machine learning approach from his PhD to biomedicine he quit his job and co-founded a data science company for biomedicine. He regularly serves as a data science consultant and as an instructor in data science boot camps such as Science-to-Data-Science (S2DS) in London. Additionally, based on his entrepreneurial experience in digital health he is a distinguished speaker reporting about data science in biomedicine at public events, e.g. Europäischer Gesundheitskongress, BPI - Bundesverband der Pharmazeutischen Industrie e.V..
 
<A HREF="https://www.linkedin.com/in/marco-schmidt-7527aa19/" TARGET="_blank" TITLE="LinkedIn" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Dr. Marco Schmidt</A> 
Marco is a biochemist with a strong expertise in chemical biology. In his PhD thesis he developed novel methods for drug discovery. That was honored by the German Chemical Society with the Klaus Grohe Prize of Medicinal Chemistry. Afterwards he joined the University Chemical Laboratory Cambridge UK, working for the Bill &amp; Melinda Gates Foundation on novel anti-tuberculosis drugs. During his stay at the University of Cambridge Marco was awarded with a prestigious Marie Curie Fellowship of the European Commission for the development of a novel drug class targeting RNA interference.
 
Marco provides almost a decade of experience in the biotech ecosystem. He initiated or joined several biotech spin-offs from academia, always with a focus of generating a business model for a new technology. Marco shares his expertise: He edited and co-authored several books and articles dealing of novel technologies in drug discovery and development. From there, he is a sought-after partner in research translation. 
 
<HR>
 
Agenda
9:30
Welcome - Introduction &amp; Expectations 
10:00
AI and data science: hype or disruptive innovation? Lessons learned from digital transformation in other industries – Lutz Haase
11:00
Deep dive into data science: Giving an understanding of artificial intelligence and machine learning – Dr Jörn Klinger
<UL>
<LI CLASS="MsoNormal">What is intelligence? What is artificial intelligence?</LI>
<LI CLASS="MsoNormal">What is learning? What is machine learning?</LI>
<LI CLASS="MsoNormal">Supervised, unsupervised, reinforcement learning</LI>
<LI CLASS="MsoNormal">Code examples: classification, dimension reduction, cross validation, ensemble learning</LI>
<LI CLASS="MsoNormal">Data mining &amp; natural language processing (NLP)</LI>
<LI CLASS="MsoNormal">Deep learning and capsule networks </LI>
</UL>
13:00 - 14:30 
Lunch
14:30
Real-world AI business cases in pharma – Dr Marco Schmidt
<UL>
<LI>What is behind “Finding new drugs faster and cheaper with AI”?</LI>
<LI>The multiple testing problem with biomedical data</LI>
<LI>Explanatory vs. predictive power for clinical data </LI>
<LI>Applying natural language processing (NLP), deep learning, classification, and explanatory vs. predictive power to the pharma value supply chain</LI>
<LI>Showcase 1: natural language processing (NLP)</LI>
<LI>Showcase 2: deep learning</LI>
<LI>Showcase 3: classification </LI>
<LI>Showcase 4: explanatory vs. predictive power (real world data and evidence)</LI>
<LI>Wrap-up: data-driven business cases in pharma</LI>
</UL>
 
16:00-17:00
Open Exchange - Ask the Experts 
17:00 
Closing &amp; Dinner
 
Please Note: 
<UL>
<LI>As this is an exclusive event we have a maximum capacity of 10 guests available.</LI>
<LI>Based on the preference of the participants the workshop will be held in English or German.</LI>
</UL>

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Data-Driven Business Models In Pharma

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