the MPOC Foundation is in partnership with Savannah, a machine learning and artificial intelligence company, to develop a way to better identify people with chronic obstructive pulmonary disease (COPD) who have not yet been diagnosed or are at high risk of contracting the disease.
The anonymized data in electronic medical records will be used to create a model that could predict who is likely to have or develop COPD. He will also be looking for patterns of drug use that may be linked to better clinical outcomes.
“Our new partnership with Savana will complement and extend the ongoing research program of the COPD Foundation – particularly in finding young people with the disease and understanding how they are diagnosed and treated”, Ruth Tal-Singer, PhD, president and scientific director, said in a Press release.
COPD360Net, the foundation’s program to support the development of digital health tools, medical devices and therapies, will use the predictive model resulting from this partnership.
“Through our network of digital health and therapeutic accelerators, COPD360Net, we are poised to conduct innovative clinical trials where we can use the predictive model developed from this relationship to make a difference for our community,” said Tal -Singer.
Artificial intelligence focuses on building machines that are trained to perform tasks faster than people can perform, albeit much slower. To train machines, researchers use deep learning, a type of machine learning that looks for patterns in data.
EHLad, a software technology developed by Savana, uses natural language processing – a branch of computing that deals with the interaction between computers and written and spoken human language – to understand what is written in electronic medical records. .
It also uses deep learning to automatically process, structure and create a predictive model based on the information available. The free-text, unstructured fields of electronic medical records, in particular, are believed to contain a large amount of useful clinical information, including signs and symptoms of disease.
As such, they can be a rich source of concrete evidence, helping researchers better understand how patients behave or respond to specific treatment in everyday clinical practice.
Importantly, the methodology ensures that all personally identifiable information has been removed from records before use, or anonymized, in order to protect the privacy and security of patient data.
“We are absolutely delighted to support the work of the COPD Foundation,” said Ignacio Medrano, Founder and Chief Medical Officer of Savana.
“A partnership like this, with a US-based non-profit health organization, is the first of its kind for Savana. It marks another milestone in our growing international ecosystem of researchers, research institutes and sponsors seeking to use deep and innovative technologies. [real-world evidence] studies to identify unmet needs, gain new knowledge and answer new questions about respiratory health and many other specialties, ”added Medrano.
A machine learning approach from Savana is also used in BigCOPData, a European Commission-funded study to identify factors associated with hospitalization in COPD patients in North America and Europe. It aims to create a predictive risk model for hospitalizations in this patient population.