• Dânia Meira

#datalift use cases in production in healthcare

Let's break the proof-of-concept cycle and productionize data analytics and machine learning.

With #datalift, we show you how business and government can scale the data economy now.




In this post, we highlight use cases in production presented by AI Guild members who are experts in deploying in healthcare.



Personalized digital healthcare. How xbird supports people living with diabetes


Presented by Dr. Evdokia Kazimirova, Machine Learning researcher, xbird

Moderated by Dr. Marija Vlajic Wheeler, Head of Career Development Program, AI Guild


Dr. Eva Kazimirova is a Machine Learning researcher with deep domain expertise in physiology. She discusses health treatment’s personalization for a chronic disease like diabetes by integrating medical and lifestyle data. Eva shows us that doctors are starting to adjust medical therapies for each patient with data models.


For delivering personalized healthcare, we merge medical and lifestyle data to empower doctors to adapt treatment and patients to adjust their activity. Xbird is deploying a variety of Machine Learning models for tracking and processing daily activities and their correlations and causation.



Read the full use case in the #datalift ebook in Kindle.


Bioactivity prediction of molecules for drug discovery


Presented by Janina Mothes, Data Scientist, KNIME

Moderated by Aline Quadros, Senior Data Scientist, flaconi


Dr. Janina Mothes is working as a data scientist in the Life Sciences team at KNIME. Before that, she worked as an IT project manager for the IT R&D department of Bayer. She has a background in biomathematics and developed computational models to simulate signaling pathways. She holds a Ph.D. in theoretical biophysics obtained from the Humboldt University of Berlin.


The end-to-end data science process traditionally starts with raw data and ends with the creation of a model. Moving that model into daily production requires considerable extra work, causing many businesses to stumble at the gap between having created a model and putting it into production. This presentation features an easy way to eliminate the gap between the creation of data science models and their use in production. We do that on a use case of bioactivity prediction of small molecules, which is an important step in drug development. Additionally, we create a web application for the deployed model, enabling chemists to use powerful machine learning tools. This approach facilitates a more data-driven development pipeline in pharma companies and other businesses.


Watch the recorded live session here:



Privacy-preserving ML with Differential Privacy in healthcare


Presented by Andreas Kopp, Digital Advisor and AI Practitioner, Microsoft

Moderated by Fabian Harder, Projektmanager, mgo360


As a Microsoft Digital Advisor, Andreas Kopp advises Enterprise customers on the planning and implementation of digital business solutions. His focus is on applied business AI solutions, including medical imaging and fraud detection. Furthermore, he specializes in practical solutions for the responsible use of AI systems. Among these are AI interpretability and fairness, as well as differential privacy.


The COVID-19 pandemic demonstrates the tremendous importance of data for research, cause analysis, government action, and medical progress. However, for understandable data protection considerations, individuals and decision-makers are often very reluctant to share personal or sensitive data. To ensure sustainable progress, we need new practices that enable insights from personal data while reliably protecting individuals' privacy.

Pioneered by Microsoft Research and associates, differential privacy is the emerging gold standard for protecting data in applications like preparing and publishing statistical analyses. Differential privacy provides a mathematically measurable privacy guarantee to individuals by adding a carefully tuned amount of statistical noise to sensitive data. It promises significantly higher privacy protection levels than commonly used disclosure limitation practices like data anonymization. In the session, we show how to use the concept in analytics and machine learning applications.


Watch the recorded live session here:


Make #datalift Season 2 happen!


Across more than ten industries, from leading corporates to leading startups, and on to new firms, we have heard from those having use cases in production. With a loyal audience of practitioners, of which more than half have roles as Seniors, Leads, Directors, and CxO, the #datalift event series from No 1 to 5 has become the key forum for exchange on what works and what is best practice.

#datalift is the online meeting space where the audience most interested in closing the gap to deployment gathers.


The AI Guild would like to have your company as a partner in growing the ecosystem of deployment: we offer year-round visibility for your content, presence in live events, qualified leads, and analytics. You achieve recognition with professionals across Europe for your solution, team, and data strategy by supporting the drive for more deployment.


The partnership is for the whole season or for as many events as your company likes. Partnership starts from €1000 per event, and the range is to €63000 for the premium season ticket.


If you are interested, click here and book a conversation

with Dânia Meira, head of #datalift

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