Big Data and Artificial Intelligence (AI) have great potential to advance precision medicine for ME/CFS and Long COVID. Dr. Chris Armstrong and his colleagues have recently published a paper titled “Machine learning and multi-omics in precision medicine for ME/CFS”.
In this review, the team explores how machine learning and multi-omics (genomics, transcriptomics, proteomics, and metabolomics) can transform precision medicine in ME/CFS research and healthcare.
From the Desk of Dr. Chris Armstrong
Director of the Melbourne ME/CFS Collaboration
ME/CFS is a complicated disease that may benefit from artificial intelligence (AI) to analyse millions of data points on individual patients. AI methods (like machine learning) help to spot patterns in large amounts of biological and physiological data, which could make it easier to tell who might have ME/CFS, to help identify the cause of the disease and to perhaps distinguish patients that might benefit from specific treatments. But AI has challenges too, like making sure it finds real patterns instead of just random ones.
Accumulating large amounts of biological and physiological data means collecting data in many ways, via wearables (like FitBit or Garmin) or collecting blood samples for data on genes, proteins, small chemicals, etc. By putting all this information together, scientists hope to find better ways to diagnose ME/CFS and understand how it affects people differently. However, this kind of research requires large amounts of patients, which can be difficult to accumulate and to maintain quality control of the data.
Even though there are challenges, the future looks hopeful. Researchers are collecting large amounts of patient data, using AI to combine information, and working together to solve this mystery. These efforts could lead to better tests to diagnose ME/CFS and, one day, treatments that actually work. There’s still a lot to learn, but new technology is helping scientists get closer to understanding this illness.
Read the full publication here.
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