Artikel

Machine learning for disease management

Digital tools and machine learning in disease management

Dr. Ameet Bakhai, a cardiologist at Royal London Hospital, reflects on his publication, where by applying machine learning techniques to the collected datasets, the prediction of development of atrial fibrillation with 80% accuracy was achieved.

“And that has an 80% prediction power of you developing atrial fibrillation in the next five years without needing any new blood tests, without even needing a new ECG.” – he argues. - So, COVID-19 has made us innovate very rapidly. Almost 80 to 90% of our work has now become remote management.”

In the future, digital technologies are going to allow us to not only see patients remotely now, but to capture trends in the patient behavior. By implementing algorithms, digital technology will allow us to actually see trends and allow us to diagnose not only the old diseases but new diseases. 

It’s an exciting new time where we’re forced to innovate and we're able to innovate with new tools.  Actually, what’s really interesting is that healthcare has been slow to innovate in the past, but the COVID-19 crisis forced us all to pick up innovation faster - the patients, the clinicians, the industries, the healthcare providers. 

Why? “You know, everybody wants to do things in a smarter way. – Dr Bakhai concludes. – “that prevents disease, keeps them safe and improves their quality of life.”

To hear more about Dr Bakhai’s publication, watch the video above.

Key facts
  • Dr Bakhai introduces us to the publication where by applying machine learning techniques to the collected datasets, the prediction of development of atrial fibrillation with 80% accuracy was achieved
  • We are starting to witness the “era of personalized prevention and prediction”
  • With the help of machine learning techniques, we can not only diagnose CV disease, but also predict who is going to develop CV disease
  • The power of machine learning has arrived, where we can actually make decisions, make an impact, or start doing prevalence studies and prevention studies.