一种新的机器学习方法,用于从摘要中系统地提取慢性病并发症
Eszter Sághy1, Mostafa Elsharkawy2, Frank Moriarty3
1Faculty of Pharmacy, University of Pécs, Pécs, Hungary.
Frontiers in digital health
|February 21, 2025
概括
这项研究开发了一种机器学习工作流程,以识别影响慢性病 (CKD) 发展和进展的疾病,发现68种并发症. 这有助于更好的患者监测和早期检测那些处于危险的.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 慢性病 (CKD) 是一个日益严重的全球健康问题,由于微妙的早期症状,往往被诊断不足,导致发病率和死亡率增加.
- 了解CKD并发症对于识别风险人群,优化治疗和改善患者的治疗结果至关重要.
研究的目的:
- 开发一个有效的机器学习 (ML) 工作流程,用于文本分类和实体关系提取.
- 编制影响CKD发展和进展的疾病的综合清单.
主要方法:
- 分析了来自Embase图书馆的39,680份与CKD相关的摘要.
- 利用在人类标记数据上训练的多个ML分类器,使用主动学习进一步优化最佳性能模型 (SVM).
- 采用一种新的实体关系提取方法来从选定的摘要中识别和列出相关疾病.
主要成果:
- 一个优化的ML工作流成功地在15个ICD-10疾病组中识别了68种共同疾病,这些疾病有助于CKD.
- 该研究区分了对CKD有直接和间接因果影响的疾病,并将精神分裂症作为间接影响的一个例子.
结论:
- 这些发现为未来的CKD研究提供了基础,使得更广泛的伴随性疾病可以纳入预后模型.
- 这项工作通过加强患者监测,预防策略和早期检测易患CKD发展或进展的个体来支持临床实践.
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