“Application of machine learning and text analytics in a cohort of people with
ME/CFS and people with Long Covid to capture disease severity, potential
associations and sequential patterns of events”
Principal Investigator: Efthymios Kalafatis, Data Scientist and Predictive Analytics Consultant

EFTHYMIOS KALAFATIS

“Application of machine learning and text analytics in a cohort of people with
ME/CFS and people with Long Covid to capture disease severity, potential
associations and sequential patterns of events”
Principal Investigator: Efthymios Kalafatis, Data Scientist and Predictive Analytics Consultant

efthymios-kalafatis

Efthymios Kalafatis, MSc, (Principal Investigator) is a data scientist with 21 years of experience in data analysis, including the application of artificial intelligence and machine learning methods in the healthcare, banking, social media and other sectors. 

Kathleen Mudie, MSc, is a research senior manager at the Solve ME/CFS Initiative with epidemiological experience in ME/CFS as well as other chronic conditions, such as chronic kidney disease, hypertension and diabetes.

Kalafatis will lead the project, which will capitalize on the power that health tracking has to help us better identify meaningful patterns in chronic illness research by applying powerful machine learning and analysis methods to better understand symptoms, identify potential associations with treatments and life events and examine models of temporal patterns.   

Study Summary

We propose using cutting-edge machine learning and text analytics methods in data collected from participants of the You + ME Registry that will enable fast and cost-effective research into ME/CFS, Long Covid and other diseases.

Our goal is to combine questionnaire data and data from the mobile symptom tracking app in order to identify across all participants patterns with symptoms, treatments, life events and activities. We will also look at how these patterns can change over time at the individual level. Findings of this study have the potential to highlight areas of research necessary for furthering our understanding of these diseases, both in research and in clinical practice.

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