Scholarlink

<p>We developed machine learning models to predict the presence of AMR organisms in blood cultures obtained at the first patient encounter, offering a new and inspiring direction for antimicrobial resistance management. Three supervised machine learning classifiers were used: penalized logistic regression, random forest, and XGBoost, which were used to classify five AMR organisms: ESBL, CRE, AmpC, MRSA, and VRE. The random forest and XGBoost models performed best, with AUC-ROC values of 0.70 and 92.9% negative predictive value, respectively. The multi-class random forest model’s AUC-ROC values ranged from 0.80–0.95. Our models highlight how the combination of ADI and SVI increased the predictive power. This approach could reduce costs and mitigate the global public health threat posed by antibiotic-resistant infections. Machine learning techniques can predict antimicrobial-resistant infections in suspected cultures using patient data from EHRs, enabling clinicians to make targeted prescribing decisions and mitigate resistance development.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating socioeconomic deprivation indices and electronic health record data to predict antimicrobial resistance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月28日

查看全文
  • Marlon I. Diaz,
  • Lauren N. Cooper,
  • John J. Hanna,
  • Alaina M. Beauchamp,
  • Tanvi A. Ingle,
  • Abdi D. Wakene,
  • Zachary Most,
  • Trish Perl,
  • Chaitanya Katterpalli,
  • Tony Keller,
  • Clark Walker,
  • Christoph U. Lehmann,
  • Richard J. Medford

摘要

We developed machine learning models to predict the presence of AMR organisms in blood cultures obtained at the first patient encounter, offering a new and inspiring direction for antimicrobial resistance management. Three supervised machine learning classifiers were used: penalized logistic regression, random forest, and XGBoost, which were used to classify five AMR organisms: ESBL, CRE, AmpC, MRSA, and VRE. The random forest and XGBoost models performed best, with AUC-ROC values of 0.70 and 92.9% negative predictive value, respectively. The multi-class random forest model’s AUC-ROC values ranged from 0.80–0.95. Our models highlight how the combination of ADI and SVI increased the predictive power. This approach could reduce costs and mitigate the global public health threat posed by antibiotic-resistant infections. Machine learning techniques can predict antimicrobial-resistant infections in suspected cultures using patient data from EHRs, enabling clinicians to make targeted prescribing decisions and mitigate resistance development.