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Use clinical variables from the EMR of patients to predict antibiotic resistance.
Antimicrobial resistance (AMR) has become a serious global health problem. In intensive care units, the severity of the threat of ADR is highlighted by the fact that between 30% and 60% of antibiotics prescribed are considered unnecessary, inappropriate or suboptimal (1)(2). This high level of misuse not only undermines the effectiveness of treatments but also catalyzes the advancement of resistant bacterial strains. Furthermore, inadequate access to antibiotics is alarming, as evidenced by the high percentage of medications sold without a prescription: 27% in urban areas and 8% in rural areas, contributing to the AMR crisis (3)(4). Furthermore, leniency in the dispensing of antibiotics by pharmacies, where 51.7% do so without the proper prescription (5)(6), continues to fuel this global health risk that threatens to reverse decades of medical progress.
To address the issue of antimicrobial resistance, the “AMRForecast AI” model has been devised to guide the medical community in prescribing precise antibiotic therapies. By analyzing clinical and genomic data, AMRForecast AI makes it possible to predict the effectiveness of antibiotics.
The model integrates two main data sets. Genomic Data Resource (8) collects genomic sequences from clinical laboratories, essential for understanding pathogen evolution. Unstructured clinical records come from patients' EHRs (7) and contain a wealth of information including demographics, diagnoses, and treatment outcomes. These data sets provide a comprehensive view of antimicrobial resistance, allowing AI to detect and analyze resistance trends.
Antimicrobial resistance (AMR) has become a serious global health problem. In intensive care units, the severity of the threat of ADR is highlighted by the fact that between 30% and 60% of antibiotics prescribed are considered unnecessary, inappropriate or suboptimal (1)(2). This high level of misuse not only undermines the effectiveness of treatments but also catalyzes the advancement of resistant bacterial strains. Furthermore, inadequate access to antibiotics is alarming, as evidenced by the high percentage of medications sold without a prescription: 27% in urban areas and 8% in rural areas, contributing to the AMR crisis (3)(4). Furthermore, leniency in the dispensing of antibiotics by pharmacies, where 51.7% do so without the proper prescription (5)(6), continues to fuel this global health risk that threatens to reverse decades of medical progress.
To address the issue of antimicrobial resistance, the “AMRForecast AI” model has been devised to guide the medical community in prescribing precise antibiotic therapies. By analyzing clinical and genomic data, AMRForecast AI makes it possible to predict the effectiveness of antibiotics.
The model integrates two main data sets. Genomic Data Resource (8) collects genomic sequences from clinical laboratories, essential for understanding pathogen evolution. Unstructured clinical records come from patients' EHRs (7) and contain a wealth of information including demographics, diagnoses, and treatment outcomes. These data sets provide a comprehensive view of antimicrobial resistance, allowing AI to detect and analyze resistance trends.
