从基于机器学习的血液计数和采访中识别儿童细菌性胃肠炎
1Department of Pediatrics, Haibara General Hospital, Shizuoka, JPN.
Cureus
|August 21, 2023
概括
机器学习模型可以帮助区分细菌性胃肠炎和病毒性原因在使用血细胞计数的儿童. 这种方法在识别细菌感染方面提供了适度的准确性,有助于临床决策.
科学领域:
- 儿科胃肠病学 儿科胃肠病学
- 医疗信息学 医疗信息学
- 机器学习在医学中的应用
背景情况:
- 在儿科护理中,区分细菌和病毒性胃肠炎至关重要.
- 机器学习 (ML) 为识别细菌性急性胃肠炎 (AG) 提供了一个潜在的工具.
研究的目的:
- 开发和评估一种ML模型,用于识别儿童的细菌AG.
- 用血细胞计数和采访数据进行诊断分类.
主要方法:
- 采用了决策树分类器,对已发表研究的数据进行了培训.
- 分析了164名患有胃肠炎的儿童 (1-108个月) (112个细菌AG,52个病毒AG).
- 使用LASSO进行特征选择,并通过五倍交叉验证评估性能.
主要成果:
- ML模型实现了0.80的曲线下面积 (AUC),表明性能中等.
- 关键的预测特征包括血小板-淋巴细胞比率,乙素细胞计数和白细胞计数.
结论:
- 细菌AG可以通过ML通过血细胞计数以中等准确度估计.
- 这些发现可以帮助区分细菌AG在儿童呈现胃肠道症状.
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