Prevents antimicrobial IV to oral switch decisions being missed which can reduce patients’ length of stay, their chance of hospital acquired infections and antimicrobial resistance.

For any patient who is on IV antibiotics the model takes the patients routinely collected clinical parameters during their stay as input features and outputs on each day if they are suitable for switching to oral treatment or not. This can be used by healthcare professionals during their usual daily wards rounds to assess if a patients antibiotic treatment should be changed or not.

  • Antimicrobial stewardship
  • Infection prevention
  • Microbial diagnostics
  • Antimicrobial compound/strategy
  • Removal antibiotics/bacteria

  • Bacteria
  • Viruses
  • Fungi
  • Yeasts
  • Parasites

  • Human
  • Veterinary
  • AgriFood
  • Environmental
  • Other

Development stage:
  • Development
  • Validation
  • Research
  • Market entry
  • Marketed product

  • Academia
  • Company
  • Institute
  • NGO
  • Government

  • License
  • Co-develop
  • Outsource
  • Joint Venture
  • Sell

Funding organisation:
  • CARB-X
  • FIND
  • OTHER / NA

Infectious disease area:
  • UTI
  • STI
  • BSI
  • RTI
  • GII
  • SSTI
  • CNSI
  • IAI
  • SSI

Geographic origin:
  • Eurasia
  • North America
  • South America
  • Africa
  • Oceania

We are open to having discussions with any potential partner who is interested in our AI technology for antimicrobial stewardship. Hospital institutions are of great interest for future prospective studies and trial implementation.

Imperial College London

AI for predicting the risk of infection, side effects, and readmission as well as to support antibiotic cessation decisions.

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