Scholarlink

<p>Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe, limb-threatening infections with overlapping features, making early differentiation challenging. To address this, we developed and validated an explainable machine learning model using routine blood biomarkers from a retrospective, multi-center cohort of 3415 patients (579 NF, 2836 OM). Data from a primary center were used for model development, with data from a second center serving as an independent external testing cohort. Systematic evaluation identified an optimal 10-biomarker LightGBM model that achieved outstanding discrimination on the external cohort, with an AUC of 0.926. Beyond its high accuracy, explainability analyses confirmed the model’s predictions are driven by robust, clinically relevant markers of severe inflammation and metabolic dysfunction, reinforcing its trustworthiness. The final model was deployed as a publicly accessible web tool for real-time risk stratification. This work provides a powerful, externally validated, and explainable AI framework to augment clinical judgment, with strong potential to reduce diagnostic delays and improve outcomes for these devastating infections.</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

Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

查看全文
  • Parhat Yasin,
  • Zubaidanmu Aizezi,
  • Shiming Dong,
  • Yasen Yimit,
  • Alimujiang Yusufu,
  • Wei Xiang,
  • Zhoujun Zhu,
  • Haopeng Luan,
  • Xinghua Song

摘要

Necrotizing fasciitis (NF) and osteomyelitis (OM) are severe, limb-threatening infections with overlapping features, making early differentiation challenging. To address this, we developed and validated an explainable machine learning model using routine blood biomarkers from a retrospective, multi-center cohort of 3415 patients (579 NF, 2836 OM). Data from a primary center were used for model development, with data from a second center serving as an independent external testing cohort. Systematic evaluation identified an optimal 10-biomarker LightGBM model that achieved outstanding discrimination on the external cohort, with an AUC of 0.926. Beyond its high accuracy, explainability analyses confirmed the model’s predictions are driven by robust, clinically relevant markers of severe inflammation and metabolic dysfunction, reinforcing its trustworthiness. The final model was deployed as a publicly accessible web tool for real-time risk stratification. This work provides a powerful, externally validated, and explainable AI framework to augment clinical judgment, with strong potential to reduce diagnostic delays and improve outcomes for these devastating infections.