project . 2020 - 2021 . Closed

Artificial Intelligence for Missing Data Imputation in Electronic Medical Records

UK Research and Innovation
  • Funder: UK Research and InnovationProject code: NE/T013982/1
  • Funded under: NERC Funder Contribution: 10,312 GBP
  • Status: Closed
  • Start Date
    31 Mar 2020
    End Date
    29 Sep 2021
Description
Health systems in the UK and Canada have made extensive use of Electronic Medical Records (EMR) for many years as an integral part of their operations. However, whilst digitally recorded data exists, their use as the basis of a "learning health system" whereby continuous improvements in patient experience, hospital operations, and quality of care has are made by collating and examining data and evidence to improve all these areas. However, real-world EMR data can be very challenging to handle. One significant contribution to these difficulties is data quality. Missing data is a particular issue, with rates of missingness of between 10-30% for some reco...
Description
Health systems in the UK and Canada have made extensive use of Electronic Medical Records (EMR) for many years as an integral part of their operations. However, whilst digitally recorded data exists, their use as the basis of a "learning health system" whereby continuous improvements in patient experience, hospital operations, and quality of care has are made by collating and examining data and evidence to improve all these areas. However, real-world EMR data can be very challenging to handle. One significant contribution to these difficulties is data quality. Missing data is a particular issue, with rates of missingness of between 10-30% for some reco...
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