Becoming a Healthcare Xplorer is so much more than solving a data challenge: It is a great opportunity to boost your career, to meet inspiring people and last but not least, to realise your idea together with Roche.

Challenges from batch 3 (2021)

In realisation 

Augmented patient pathways in oncology

— Focus: PHC

Real world population-level oncology data contains information that can change the way patients are treated. Do you want to pave the way to the future of healthcare?

In realisation 

Develop machine learning-based methods for early anomaly detection in diagnostics instrument and assay data

— Focus: PHC

Millions of patients around the world rely on the precision and accuracy of our diagnostics devices and lab instruments every day. We are curious to learn from your ideas: how could we monitor the quality of our products and detect emerging issues as early as possible?

In realisation 

Generating synthetic patient-reported outcomes to foster collaboration in clinical settings

— Focus: PHC

Shifting the focus to patients in the clinical workflow is key for a meaningful and patient-centric healthcare transformation. For this, we would like to explore the changes in the patient’s quality of life along with the care flow. We foresee that data sharing will be key to foster collaboration. Would you take that journey with us?

— Mentors Batch 3

— Mentors Batch 3

Marta Batlle
Start-IT, Machine Learning

Celia Bel
Senior Data Scientist, Roche Information Solutions

Danilo Guerrera,
Data Scientist, Machine Learning

Dr. Michael Laimighofer
Team Lead – Data Science @ The Cube

Dr. Carsten Magnus
Principle Data Scientist, Roche Information Solutions

Enrique Vidal Ocabo,
Senior Data Scientist, Roche Information Solut

Dr. Lara Schneider
Data Scientist @ The Cube

Challenges from batch 2 (2021)

In realisation 

Predicting drug effects in a multimodal biological network

— PHC

The fundamental understanding of the human organism and how it is perturbed by variants and drugs forms the basis of delivering tailored care. How can we predict the specific effect a drug has on its known cellular target(s) based on its cellular interaction network?

In realisation 

Explore the future of sensing

— PHC

How will trends affect lives and behaviors and influence the daily lives of our patients? How connected will our world be in the future and what could that mean for the future solutions for diabetics

In realisation 

A deep learning-based approach for detection of neurological disease patterns using a draw-a-shape test

— PHC

Interested in machine learning, and analysing digital health datasets to uncover hidden disease progression? Want to make a tangible impact on the lives of people living with neurological diseases? Then help us apply deep learning methods to develop better digital measurements of impairment!

In realisation 

Non-invasive liquid level detection

— Lab Automation

Do you want to help improve the testing capacity of medical laboratories and hospitals? Do you want to have a direct impact on society’s ability to test for and thereby track diseases faster? Have a look at the data set and convince us of your solution proposal!

In realisation 

Graph-based Bottleneck Analysis in a complex production environment

— Process and Manufacturing Analytics

You love diving really deep into data and business topics which are linked to real value chain problems. You like to work on real, incomplete, dirty data sets, using complex methods while explaining it in simple words. Welcome to the world of enterprise data science!

— Mentors Batch 2

— Mentors Batch 2

Frank Dondelinger
Data Analysis Lead MS, Digital Biomarkers, pREDi

Marcin Elantkowski
Principal Associate Data Analyst, Digital Biomarkers, pREDi  

Dr. Charlotta Fruechtenicht
Senior Data Scientist, PHC Analytics

Romain Guerre
Software Engineer, Sample Quality

Dr. Frank Kienle
Digital Strategy Manager, Materials & Business Process Management

Dr. David C. Krey
Senior Innovation Lead, Diabetes Care Global R&D Innovation

Jim Lefevere
International Business Leader Pre-Diabetes & OAD, Diabetes Care Global Strategy & Customer Solutions

Challenges from batch 1 (2020)

In realisation 

Impact of checkpoint inhibitor therapies on a patients’ immunogenicity status

Are you curious about cancer immunotherapy everyone in the oncology world is talking about? Do you love using and advancing your skills to analyze complex data and shedding light on current research questions?

In realisation 

Computational deconvolution for patient stratification in the context of non-small cell lung cancer

Computational methods can help to unravel the complexity of the tumor microenvironment of individual patients. We ask for your help to assess such methods to enhance our understanding of cancer heterogeneity and to improve patient classification.

In realisation 

Development of a digital companion for breast cancer patients

Cancer patients often do not know what to do when they get their initial diagnosis and are often overwhelmed with the situation. We are curious to learn from your ideas how to enable patients to navigate through the jungle of information, challenges and hurdles, so that they can be in the driver’s seat of their patient journey inside the healthcare system.

In realisation 

Incorporating Natural Language Processing (NLP) Workflows into an open-source genomics cancer portal

Support clinicians in decision-making processes through easy access of mutation dependent targeted therapy options by using NLP.

— Mentors Batch 1

— Mentors Batch 1

Dr. Franziska Braun

Dr. Franziska Braun
Senior Data Scientist, Pharma Research and Early Development Informatics

Dr. Markus Bundschus

Dr. Markus Bundschus
Head Data Science Technologies, Data Office

Dr. Lars Hummerich

Dr. Lars Hummerich
Head of Oncology Innovations, Oncology Innovations

Dr. Jan Rieckmann

Jan Riekmann
Group Lead Data Insights, Data Office