Volv Global Blog

Posts by

Christopher M de M Rudolf

Christopher is the founder & CEO of Volv Global SA based in Lausanne, an applied data science company, that creates cutting edge approaches to solving difficult problems in healthcare and life sciences. He is passionate about innovation, thinking aloud/allowed and entrepreneurship and applying it in the healthcare and life sciences industry to help improve the patient experience of living with disease. He has over 30 years of experience as a technology entrepreneur and independent business advisor, having worked for many Blue-Chip organisations helping solve their critical global scale data problems. Interested in: Like-minded curious people, collaborative cross-disciplinary thinking, rare diseases, and next generation healthcare.

Quantifying Predictive Power of Features in Electronic Health Record Models – the Volv inTrigue way

From predictive to interpretable models

At Volv we provide the insights on our approaches and methodology 

Quantifying Predictive Power of Features in Electronic Health Record Models – the Volv inTrigue way


When we work on complex prediction models with our inTrigue methodology, we are often asked to help clinicians and others to interpret these models by listing the patient features (attributes) which are used by the model to form its predictions. And indeed, generating a list of predictors ranked by their ‘importance’ in a model can translate to improved interpretability and clinical impact. However, there is some work that needs to be carefully considered in order to produce tooling that is derived from complex models that can provide real benefit in a clinical situation. So Rich Colbaugh and I decided to discuss this for you, our audience.

Interpretability is a theme that often surfaces when considering data science model outputs, and the issue of a 'black-box' system is often cited 

as a challenge for customers and is discussed at many conferences. As it is important to deliver real world results, we discuss here what Volv does to create interpretable models of true utility. The journey from complex modelling to interpretable models is however not necessarily simple when dealing with real-world solutions involving highly dimensional messy data.

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This case study derives from a pharmaceutical company that approached Volv to develop a prediction model to find patients suffering from a rare disease

Finding Patients for Rare Diseases: A Case Study

The Case Study Problem

This case study derives from an ongoing Volv engagement, which started in July 2017. A pharmaceutical company approached Volv Global to develop a prediction model to identify additional patients suffering from the rare disease treatable by its specialist medicine.

This pharmaceutical company faced four difficulties: the disease prevalence was one in a million of population; specialist clinicians were able to diagnose the disease with no more than 76% accuracy; only one in four patients were ever identified; and those that were identified, were done so generally after six years of misdiagnoses. To compound these four difficulties, the pharmaceutical company was unable to provide medical records for any already diagnosed patients.

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