From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review.
The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 15 |
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| Autores principales: | , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
8/1/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179604833&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179604833 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/1/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179604833 179604833 179604833 10.1007/s10916-024-02089-5 179604833 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review. aug: au: de la Lastra, José M. Pérez Wardell, Samuel J. T. Pal, Tarun de la Fuente-Nunez, Cesar Pletzer, Daniel affil: https://ror.org/028ev2d94 Biotechnology of Macromolecules, Instituto de Productos Naturales y Agrobiología, IPNA (CSIC), Avda. Astrofísico Francisco Sánchez, 3, 38206, San Cristóbal de la Laguna, (Santa Cruz de Tenerife), Spain sug: subj: Drug Resistance, Microbial Evaluation Artificial Intelligence Machine Learning Algorithms Data Management Decision Making, Clinical Infection Microbiology Infection Drug Therapy Antibiotics Therapeutic Use Microbial Culture and Sensitivity Tests Treatment Outcomes Drug Discovery Drug Administration Infection Diagnosis Severity of Illness Evaluation Data Quality Infection Pathology Antimicrobial Stewardship Individualized Medicine Antibiotics Administration and Dosage Antimicrobial Peptides ab: The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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