Job Detail

Healthcare Xplorersis a new way and platform to foster academic collaborations in the space of data. The focus is on real challenges from Roche business areas where data plays a crucial role, and where new perspectives from academia complement our industry perspective. Fresh thinking is key to develop new solutions – and this is where you as a student or PhD come in. Do you love data? Are you an out-of-the-box thinker? Then join us to make a change!

Find out more:

healthcare-xplorers.com/challenges/generating-synthetic-patient-reported-outcomes-to-foster-collaboration-in-clinical-settings/

Question to be solved

How can we generate PROs synthetic data? How can we check that the synthetic data is meaningful? Could we scale it up and standardize/automate the process for this type of data?

General Background

We would like to understand the changes in quality of life along with the oncology care flow, to support patients in that journey and hopefully decrease their psychological burden. To do so, we would rely on Patient-Reported Outcomes (PROs) data. This data is sensitive. Thus we need to find a way to generate meaningful synthetic data that resembles the original as much as possible. That way, data and knowledge can be shared more easily, paving the road to fruitful collaborations. Being able to share would enable us to grow better and faster in the long term.

Data Types & Technologies

• Clinical trial data from Roche


• Patient-Reported Outcomes


• Generative Adversarial Networks (GANs) and Variational Autoencoders


• Tensorflow 2 and PyTorch

Supporting Material or Links

• Symptom Monitoring With Patient-Reported Outcomes During Routine Cancer Treatment: A Randomized Controlled Trial. J Clin Oncol. 2016. ascopubs.org/doi/abs/10.1200/JCO.2017.35.18_suppl.LBA2


• Incorporating Patient-Reported Outcome Measures into Breast Surgical Oncology: Advancing Toward Value-Based Care. The Oncologist 2019.  europepmc.org/article/med/31848315

Some works related to GANs:

• Ian Goodfellow et al., 2014. Generative Adversarial Networks. arxiv.org/abs/1406.2661


• Beaulieu-Jones, B. K. et al. 2019. Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing. www.ahajournals.org/doi/full/10.1161/CIRCOUTCOMES.118.005122


• Asokan S., Seelamantula, C.S. 2020. Teaching a GAN what not to learn. arxiv.org/abs/2010.15639

Needed Skills

• programming skills (R and/or python, Pytorch and 0)


• Interest in patient’s quality of life


• Interest in data privacy and sharing


• Technical background


• good analytical/mathematical thinking

Mentors

Marta Batlle
Start-IT, Machine Learning

Danilo Guerrera
Data Scientist, Machine Learning

Enrique Vidal Ocabo 
Senior Data Scientist, Roche Information Solutions

Form of Cooperation

• Preferred option: 6 months full-time internship


• Open for other possible formats

How to present your Idea

Choose your preferred way to present your approach. We would love to ask some questions about it. Be aware you only have 10 minutes

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