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Machine learning to combat AMR through the integrated analysis of VetCompass and laboratory data

Prof David Brodbelt (RVC), Prof Yonghong Peng (Manchester Metropolitan University), Mr Noel Kennedy (RVC), Dr Dan O’Neill (RVC), Dr Collette Taylor (RVC), Prof David Church (RVC) and Dr Sam Clifford (LSHTM) - £30,000
1 October 2019 to 31 March 2021 (Data Grant BSA30)

Aims: To develop data linkage machine learning methodology for external laboratory data linkage to pseudo-anonymised clinical records.

Objectives:

  1. Link APHA and IDEXX laboratory testing for canine leptospirosis to pseudo-anonymised VetCompass electronic patient records
  2. Evaluate machine learning process performance for data linkage.
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