AMRx
Patient data based on an exhaustive list of features including presenting symptoms, comorbidities and clinical history was prospectively collected after informed consent from seven hospitals located in south India. This data was curated and used for the development of prediction model that can accurately predict UTI in suspected patients using only a set of clinical information. Further, machine learning models were developed which could predict whether a patient with a set of symptoms and comorbidities could be infected with an Enterobacteriaceae pathogen or not. Finally, if a patient is predicted to have an Enterobacteriaceae infection, an additional set of algorithms were developed to predict the infecting Enterobacteriaceae to be a ESBL-positive or negative among inpatients and outpatients separately and/or b Nitrofurantoin resistant, and/or c amikacin resistant, and/or d Piperacillin_Tazobactum resistant and/or e Cefoperzone_Sulbactum resistant, and/or f Ciprofloxacin resistant, and/or g Cefepime resistant, and/or h Gentamicin resistant and/or i Ceftriaxone resistant.
Upon successful implementation, AMRx would save time, effort and resources, while also ensuring early prognosis and treatment of UTIs among patients who need it.
USP: a Sample free b culture free c Instant prediction d non-invasive e Supports rationale prescription of antibiotics f clinically validated
- Innovator's Name
- Product/Technology Description
Sri Sathya Sai Institute of Higher Learning
a Startup with extremely strong foundation in the containment and surveillance of Antimicrobial resistance. b Played important role in developing AMR state action plans c Renowned scientific advisors with global exposure in AMR
- Product Name: AMRx
- Product Title: A Machine Learning Platform for Predicting Uropathogens and their Resistance for prescribing Suitable Urinary Infection Therapy
- Description: Patient data based on an exhaustive list of features including presenting symptoms, comorbidities and clinical history was prospectively collected after informed consent from seven hospitals located in south India. This data was curated and used for the development of prediction model that can accurately predict UTI in suspected patients using only a set of clinical information. Further, machine learning models were developed which could predict whether a patient with a set of symptoms and comorbidities could be infected with an Enterobacteriaceae pathogen or not. Finally, if a patient is predicted to have an Enterobacteriaceae infection, an additional set of algorithms were developed to predict the infecting Enterobacteriaceae to be a ESBL-positive or negative among inpatients and outpatients separately and/or b Nitrofurantoin resistant, and/or c amikacin resistant, and/or d Piperacillin_Tazobactum resistant and/or e Cefoperzone_Sulbactum resistant, and/or f Ciprofloxacin resistant, and/or g Cefepime resistant, and/or h Gentamicin resistant and/or i Ceftriaxone resistant. Upon successful implementation, AMRx would save time, effort and resources, while also ensuring early prognosis and treatment of UTIs among patients who need it.
- Unique Selling Point: a Sample free b culture free c Instant prediction d non-invasive e Supports rationale prescription of antibiotics f clinically validated
