Machine Learning Models as Diagnostic Tools for COVID-19

Clinical trial that studies the development of alternative diagnostic tools that reliably assess COVID-19 infection, and that are low cost and easy to administer to the entire population

Keywords: COVID-19; Machine learning; COVID-19 diagnostic

About COVID Symptom Study

The COVID Symptom Study app is a not-for-profit initiative that was launched at the end of March 2020 by health science company ZOE with scientific analysis provided by King’s College London.

By using this app you’re helping and contributing to advance vital research on COVID-19. By combining your reports with software algorithms, we are able to predict who has the virus and so track COVID infections across the UK and now other countries. 

Description of the Study:

  • Title: Validation of Machine Learning (ML) Models as Diagnostic Tools to Predict Infection With SARS-CoV-2.
  • Principal Investigator: Tim Spector.
  • Co-investigators: Sarah Berry, Claire Steves, Sebastien Ourselin, Peter Sasieni and Andrew Chan.
  • Centers of Implementation: King’s College London.
  • Study population: Individuals who are UK-based primary users of the Covid Symptom Study app, that provide informed consent to participate.
  • Study Type: Prospective observational cohort study.
  • Design: Prospective study that enables regular iteration on prediction models and continuous accumulation of validation data. The study consists of a series of phases, each lasting 14 days. Before the start of each phase (day 0), a set of machine learning models will be frozen and submitted for validation on data collected during this and subsequent phases.

Objectives of the Study:

Principal Objective: To validate machine learning models as a diagnostic tool that predicts infection with SARS-CoV-2 based on app-reported symptoms and phenotypic data, against the ‘gold-standard’ swab PCR-test. This study will take place within the Covid Symptom Study app, the free symptom tracking mobile application.

More about this Study:

Cientific context: The COVID-19 viral pandemic has caused significant global losses and disruption to all aspects of society (including health, education, and business and economic security). One of the major difficulties in controlling the spread of this coronavirus has been the delayed and mild (or lack of) presentation of symptoms in infected individuals. The profound and widespread cost of the continuing Covid-19 progression, coinciding with the lack of testing capacity, warrants the development of alternative diagnostic tools that reliably assess COVID-19 infection in the early stages of infection.

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