From Biology to Telecoms
C-BIA signed a partnership with Coevolve IT in October 2020 to further our collaborative work in the Research sector, notably in Healthcare. Here Paul Marrow, its founder, explains how he developed an interest in AI and Healthcare and the relevance of his research work in Biology and Telecoms to the field today.
Paul studied Natural Sciences and Mathematical Biology before moving into Telecoms with BT in the late 1990s to carry out R&D on networks inspired by biology. Nobody suspected then that a little over twenty years later people all over the world would be focusing on a global network of infection in the form of Covid-19.
Why was Biology relevant to Telecoms R&D?
He started his biological career researching problems where it was difficult to gather data. A combination of mathematical and computational models allowed problems to be understood over evolutionary timescales to be understood. But he then went on to work with colleagues gathering data from wild populations, where statistics allows explanation of raw data.
This combination of statistics, mathematics and software development has led to what is now called data science; thanks to the emergence of toolkits that allow analysis on scales that were previously difficult or impossible.
Working in telecoms R&D Paul was able to draw upon this interaction to make predictions about what services customers would like, and to analyse their responses to services delivered. Moving away from customers and towards the infrastructure that ensures services are available, related techniques were useful in directing network managers towards possible faults.
Biology assisted digital solutions in other ways. Where telecoms services needed to rely on resources held in data centres, software agents using biologically-inspired evolutionary computation could be used to move resources within and between data centres.
And why are insights from Telecoms R&D relevant to Healthcare tomorrow?
Where is the connection to digital solutions in Healthcare? The above was all about digital solutions, some drawing on facilities more limited than now routinely available in hospitals and across many primary care networks.
Health and social care differs from most biological research in that the recipients (patients) can often attempt to describe their experience. But clinicians still face a complexity problem in that the availability of large amounts of data about a person’s condition or their medical history may still make it difficult to make a diagnosis and respond with a care programme. This is where new ideas about how to model data, make predictions, simplify complexity or deal with missing values can make a great difference. Covid-19 has highlighted that we need to make better use of existing data for more timely research and this means better models, tools and collaborative networks.
A Future NHS
Paul Marrow brings deep insights regarding complexity in biology, mathematics, telecoms and computing and their data-driven application in a variety of contexts. He also has a strong research and partner network across the EU and is active in the FutureNHS forum. Given the challenges and opportunities that digital healthcare faces post Covid-19, he is an ideal partner and associate for us as we build on our track record in the health sector, and help to champion the better use of data, analytics and AI in the NHS.