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Prediction of Antibiotic Resistance

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.

Why it matters

  • Antimicrobial resistance (AMR) is a serious global health issue, with 30% to 60% of antibiotics in intensive care units being unnecessary, inappropriate, or suboptimal.
  • High rates of antibiotics sold without prescriptions—27% in urban areas and 8% in rural areas—contribute to the AMR crisis.
  • About 51.7% of antibiotics are dispensed without proper prescriptions by pharmacies, exacerbating the AMR problem and threatening 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.

User person:  Infectious Disease Specialists, Hospitalists, Microbiologists, Clinical Laboratory Scientists, Infection Control Practitioners, Antibiotic Stewardship Coordinators, Healthcare Administrators.

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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.


  1. World. (2020, July 31). Antibiotic resistance.; World Health Organization: WHO. Retrieved February 23, 2023,
  2. Struelens M. J. (1998). The epidemiology of antimicrobial resistance in hospital acquired infections: problems and possible solutions. BMJ (Clinical research ed.), 317(7159), 652–654.
  3. P. (2019). Antimicrobial Resistance: Implications and Costs. Infection and drug resistance, 12, 3903–3910.
  4. Bennadi D. (2013). Self-medication: A current challenge. Journal of basic and clinical pharmacy, 5(1), 19–23.
  5. Rabaan, A. A., Alhumaid, S., Mutair, A. A., Garout, M., Abulhamayel, Y., Halwani, M. A., Alestad, J. H., Bshabshe, A. A., Sulaiman, T., AlFonaisan, M. K., Almusawi, T., Albayat, H., Alsaeed, M., Alfaresi, M., Alotaibi, S., Alhashem, Y. N., Temsah, M. H., Ali, U., & Ahmed, N. (2022). Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates. Antibiotics (Basel, Switzerland), 11(6), 784.
  6. Nepal, G., & Bhatta, S. (2018). Self-medication with Antibiotics in WHO Southeast Asian Region: A Systematic Review. Cureus, 10(4), e2428.
  7. Ren, Y., Chakraborty, T., Doijad, S., Falgenhauer, L., Falgenhauer, J., Goesmann, A., Hauschild, A. C., Schwengers, O., & Heider, D. (2022). Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning. Bioinformatics (Oxford, England), 38(2), 325–334.
  8. Su, M., Satola, S. W., & Read, T. D. (2019). Genome-Based Prediction of Bacterial Antibiotic Resistance. Journal of clinical microbiology, 57(3), e01405-18.

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