训练机器学习模型以检测基于GC-MS尿液代谢组学的罕见先天性代谢错误(IEMs),用于疾病筛查。

IF 3.7 2区 医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Medical Informatics Pub Date : 2024-12-16 DOI:10.1016/j.ijmedinf.2024.105765
Haomin Li , Siyuan Gao , Dan Wu , Min Zhu , Zhenzhen Hu , Kexin Fang , Xiuru Chen , Zhou Ni , Jing Li , Beibei Zhao , Xuhui She , Xinwen Huang
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引用次数: 0

摘要

背景:气相色谱-质谱(GC-MS)已被证明是尿液分析中潜在有效的代谢分析平台。然而,由于IEM在人群中的罕见性,以及GC-MS有机酸谱分析的难度和专业复杂性,GC-MS在先天性代谢错误(IEM)筛查中的广泛应用受到了限制。方法:基于2013年至2021年在中国积累的355197个GC-MS测试用例,提出、训练和评估一个基于随机森林的机器学习模型。采用加权欠采样或过采样数据处理和分阶段建模策略来处理高度不平衡的数据,提高模型识别不同类型罕见IEM案例的能力。结果:在第一阶段模型中,仅识别阳性病例,不区分特异性IEM,筛选敏感性为0.938(如果同时包括异常病例,则为0.991)。第二阶段模型对11种特定IEMs的平均灵敏度为0.992,平均特异性和准确率分别为0.944和0.969。每个模型可视化的SHAP值解释了该模型所作鉴别诊断的基础。结论:有了足够的高质量数据,机器学习模型可以提供高灵敏度的GC-MS解释,大大提高了基于GC-MS的IEM筛选的效率和质量。
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Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC–MS urinary metabolomics for diseases screening

Background

Gas chromatography-mass spectrometry (GC–MS) has been shown to be a potentially efficient metabolic profiling platform in urine analysis. However, the widespread use of GC–MS for inborn errors of metabolism (IEM) screening is constrained by the rarity of IEM in population, and the difficult and specialized complexity of the interpretation of GC–MS organic acid profiles.

Methods

Based on 355,197 GC–MS test cases accumulated from 2013 to 2021 in China, a random forest-based machine learning model was proposed, trained, and evaluated. Weighted undersampling or oversampling data processing and staged modeling strategies were used to handle the highly imbalanced data and improve the ability of the model to identify different types of rare IEM cases.

Result

In the first-stage model, which only identified positive cases without discriminating the specific IEM, the screening sensitivity was 0.938 (or 0.991 if abnormal cases were also included). The average sensitivity of the second-stage models that classify 11 particular IEMs is 0.992, with an average specificity and accuracy of 0.944 and 0.969, respectively. The SHAP values visualized for each model explain the basis for the differential diagnosis made by the model.

Conclusion

With sufficient high-quality data, machine learning models can provide high-sensitivity GC–MS interpretation and greatly improve the efficiency and quality of GC–MS based IEM screening.
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来源期刊
International Journal of Medical Informatics
International Journal of Medical Informatics 医学-计算机:信息系统
CiteScore
8.90
自引率
4.10%
发文量
217
审稿时长
42 days
期刊介绍: International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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