Machine Learning & Prediction

The digital health revolution is here. Innovations include not only the collection and analysis of electronic health records and personal genomes, but also diverse physiological and molecular measurements in individuals at a level that has not previously been possible. Our recent studies in which we deep-profiled 109 people for a median of nearly 3 years and made 49 major health discoveries (67 if hypertension is included) affecting 53 people, shows the value of big data and active monitoring. Many health discoveries involved disease risk prediction from genome sequencing, but most involved early detection of disease before symptom onset. Many of these findings were highly impactful, such as early detection of cardiomyopathy, lymphoma, and two precancerous conditions (monoclonal gammopathy of undetermined significance and smoldering myeloma). However, the health data revolution is just beginning as more direct-to-consumer devices that measure health information become available (eg, electrocardiogram monitors for smart watches and blood pressure cuffs), omics measurements become more sensitive and facile, and new imaging technologies emerge.
How effectively society as a whole is able to capitalise on big data in health care will depend, in part, on how well these data are integrated and communicated to clinicians and the public. It will be important to have digital health information collected in a format that clinicians and consumers can easily interpret and query. Much has been written about the shortcomings of electronic health record systems. Improvements in such systems for clinicians' purposes and a new commitment to sharing personal digital health information with consumers are needed.
Currently, much of health information is organised around systems—eg, cardiovascular, gastrointestinal, and neurological. However, we expect that health information can also be organised according to underlying human molecular biology, and this might lead to novel health management approaches. For example, much as molecular information is grouped into networks based on interaction (physical or other) and co-expression data, digital health data might be grouped into entirely new interactive entities that might enable more efficient diagnoses and help to optimise disease management strategies (figure). The organisation of health information into interactive clusters and other novel methods for stratifying health data will complement existing approaches and potentially lead to improvements in health care. Tumour agnostic therapies in oncology represent early evidence of how the health-care paradigm might shift as health data management matures. We expect that there will be an important role for artificial intelligence in health data organisation and interpretation, with the caveat that clinical applications will need to be subject to rigorous validation procedures.

Big Data and Health

The importance of diversity for excellence in science

Social robots have been introduced in different fields such as retail, health care and education. Primary education in the Netherlands (and elsewhere) recently faced new challenges because of the COVID-19 pandemic, lockdowns and quarantines including students falling behind and teachers burdened with high workloads. Together with two Dutch municipalities and nine primary schools we are exploring the long-term use of social robots to study how social robots might support teachers in primary education, with a focus on mathematics education. This paper presents an explorative study to define requirements for a social robot math tutor.
Multiple focus groups were held with the two main stakeholders, namely teachers and students. During the focus groups the aim was 1) to understand the current situation of mathematics
education in the upper primary school level, 2) to identify the problems that teachers and students encounter in mathematics education, and 3) to identify opportunities for deploying a social robot math tutor in primary education from the perspective of both the teachers and students. The results inform the development of social robots and opportunities for pedagogical methods used in math teaching, child-robot interaction and potential support for teachers in the classroom.

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