The Stack Archive

How Big Data can help make better life-critical decisions

Mon 9 Jan 2017

Big Data health

matt-lovellHow do we unlock the true value of data? Matt Lovell, CTO of Centiq, argues why we need to be taking a different approach to data science…

Nowadays, collecting data has never been easier and faster, but understanding its true meaning and value still remains a challenge. IBM estimates 90% of all the data existing in the world today has been generated in the last 24 months. For data-driven organisations such as the NHS, which has disparate trusts throughout Britain generating vast amounts of data, having the ability to harness this information and analyse it correctly to get the right insights has never been more important.

In the technology profession, we may have, ironically, promoted Big Data as a panacea without truly and fully understanding the issues it can solve. There is a big opportunity here – we just need to think differently. We have plenty more to gain and exploit from Data Science but the way we apply and secure this is now more important than infrastructure.

Life-critical implications

Data Science is emerging in several industries as a business-critical practice including healthcare, which holds some of the most interesting developments. The aggregation of data sets from multiple sources, for instance, is helping to improve breast cancer’s prediction rates and enhance and accelerate drug development for various diseases.

Let’s take the work Google DeepMind is undertaking with University College London Hospital NHS Foundation Trust. By applying its DeepMind artificial intelligence to the CT and MRI scans of 700 former cancer patients, Google’s technology will quickly distinguish healthy from cancerous tissue. It is hoped a resulting algorithm will cut the time needed to design targeted radiotherapy treatments from four hours to one.

Oxford University, in collaboration with the US-based Chan Soon-Shiong Institute of Molecular Medicine, also recently launched a programme to sequence genomes of individual patients using analytics tools and connected devices. This will allow for a much more targeted, precise model of medicine for the benefit of patients.

Data Science in an environment like this has life-critical implications. It provides new ways to understand and learn more from different types of data and can help hospitals prioritise emergency care patients and treat them more effectively.

This is where the addition of Deep Learning and Machine Learning (intrinsic parts of Data Science) comes into play. These allow health organisations to create and apply structure to data through automated approaches which, until recently, would have taken years to process and analyse.

The art of fast data analytics

Data Science empowers clinicians to make better and quicker treatment decisions

Abstracting data is a process to identify the essential data characteristics that are always present and additional ones which can emerge from it. Just like a picture paints a thousand words, a data scientist can see beauty in a Big Data set.

Removing all personal details, for example, means anonymised patient records along with past and present data treatment assessments enable us to analyse and improve our understanding of future treatments. Machine Learning automates these predictions, allows many conditions to be proactively analysed and can even trigger intervention. If the trigger to intervention or assistance is critical, and the time to response is a matter of life or death, this is extremely powerful.

Essentially, Data Science empowers clinicians to make better and quicker treatment decisions so making sure both roles are working together is essential. A good example is the work Shirley Pepke, a genomics researcher, carried out to beat her own ovarian cancer. By using the results of an advanced machine learning method called Correlation Explanation (CorEx) developed by Greg Ver Steeg and her own tumor’s data, she was able to make the right choice between a menu of second-line therapies her doctors offered.

Combine this with wearable technology and we can start imagining a world where, regardless of location and providing we are contactable and connected, technology can truly help us understand so much more.

A recent report actually assessed the effectiveness of doctors to accurately predict life expectancy. With so many variables involved for patients, it is an area which can be very hard to foresee. If all medical professionals had greater access to machine learning algorithms, which would allow data entry to compare circumstances and current status, it could provide them with extremely powerful insights about the patient’s condition and guidance on the best approach to treat it.

Connecting data science and deep learning

There is untold value in the data we already have. For many organisations, there are significant repositories of data contained in warehouses and databases which Deep Learning and Data Science, when, connected together, can and does provide a much deeper understanding of our customers and lives.

There is untold value in the data we already have

Data Science helps ask questions with a different logic in different ways to understand them. Many larger enterprise ERP and business warehouse systems do not currently link these systems together, most often due to complexity in terms of technologies, processes and people. Unlocking this ability is key to organisations creating new value from existing and hugely powerful data.


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