Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A new application programming interface (API) for antimicrobial prescription support
1
Zitationen
15
Autoren
2025
Jahr
Abstract
<ns3:p>Background The escalating threat of AMR demands a paradigm shift in antimicrobial prescribing practices. The application programming interface (API) is conceived as an advanced system, integrating artificial intelligence and machine learning, to optimize clinical decision-making in the context of antimicrobial therapy. This study outlines the development and evaluation of the software, emphasizing its potential impact on antimicrobial stewardship. Methods The API was meticulously constructed in two phases. In the initial phase, an algorithm leveraging decision flow, developed by a collaboration of information technology experts, infectious disease and microbiology specialists, was designed. This algorithm accounts for a comprehensive array of variables influencing antimicrobial treatment outcomes. Subsequently, a Machine Learning model was employed to assess the probability of success for each available antimicrobial drug. The second phase involved a rigorous evaluation through ten hypothetically described clinical cases, assessed independently by five infectious disease specialists (IDP team) in a double-blinded study. Results generated were then compared with the antimicrobial prescriptions made by the IDP team. Results Utilizing the World Health Organization's AWaRe classification system as a benchmark, the API demonstrated a 50% prescription at both the Access and Watch categories, with a 0% allocation in the Reserve category. In comparison, the IDP team exhibited an 11.9% prescription in the Access, 73.9% at Watch, and 14.5% at Reserve category. Conclusion Despite potential disparities between expert opinions and the software, the proposed system, characterized by its conservative nature, holds promise in refining and validating clinical decisions. Moreover, the implementation of the API has the potential to mitigate selective pressure that contributes to antimicrobial resistance, thus fortifying antimicrobial stewardship practices.</ns3:p>
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.428 Zit.
Autoren
Institutionen
- Instituto Federal de Educação Ciência e Tecnologia do Paraná(BR)
- Pontifícia Universidade Católica do Paraná(BR)
- Universidad Marcelino Champagnat(PE)
- Secretaria da Saúde(BR)
- Hospital Nossa Senhora das Graças(BR)
- Universidade Federal de Santa Catarina(BR)
- Centro Universitário de Belo Horizonte(BR)
- Grupo Santa Casa de Belo Horizonte(BR)