A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles
A Machine Learning Approach for the Automated Interpretation of Plasma Amino Acid Profiles
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DOI：
10.1093/clinchem/hvaa134
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发表时间：
2020-09-01
影响因子：
9.3
通讯作者：
Carling, Rachel S.
中科院分区：
文献类型：
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作者：
Wilkes, Edmund H.;Emmett, Erin;Carling, Rachel S.
背景：血浆氨基酸(PAA)谱在常规临床实践中用于诊断和监测遗传性氨基酸代谢紊乱、有机酸血症和尿素循环缺陷。PAA资料的解释是复杂的，需要大量的培训和专业知识来执行。考虑到机器学习(ML)算法解释复杂临床生化数据的能力，我们试图确定ML派生的分类器是否能够解释具有高预测性能的PAA图谱。方法：我们收集了临床生化实验室中常规执行的PAA图谱数据(2084个图谱)，并使用几种ML算法开发了决策支持分类器。我们使用嵌套交叉验证(CV)程序测试了每个分类器的泛化性能，并检验了不同的子采样、特征选择和集成学习策略的效果。结果：分类器表现出良好的预测性能，测试的3个最大似然算法产生了类似的结果。性能最好的集成二分类分类器的平均精度召回率(PR)为0.957(95%CI 0.952，0.962)，性能最好的集成多类分类器的平均F4得分为0.788(0.773，0.803)。结论：本工作建立在已有的ML派生决策支持工具在临床生化实验室中的应用示范的基础上。我们的发现表明，在有待进一步的验证研究之前，这些工具可能会用于常规的临床实践，以简化和帮助解释PAA图谱。这在资源有限、工作量大的实验室中尤其有用。我们为其他实验室开发自己的决策支持工具提供必要的代码。
BACKGROUND: Plasma amino acid (PAA) profiles are used in routine clinical practice for the diagnosis and monitoring of inherited disorders of amino acid metabolism, organic acidemias, and urea cycle defects. Interpretation of PAA profiles is complex and requires substantial training and expertise to perform. Given previous demonstrations of the ability of machine learning (ML) algorithms to interpret complex clinical biochemistry data, we sought to determine if ML-derived classifiers could interpret PAA profiles with high predictive performance.METHODS: We collected PAA profiling data routinely performed within a clinical biochemistry laboratory (2084 profiles) and developed decision support classifiers with several ML algorithms. We tested the generalization performance of each classifier using a nested cross-validation (CV) procedure and examined the effect of various subsampling, feature selection, and ensemble learning strategies.RESULTS: The classifiers demonstrated excellent predictive performance, with the 3 ML algorithms tested producing comparable results. The best-performing ensemble binary classifier achieved a mean precision-recall (PR) AUC of 0.957 (95% CI 0.952, 0.962) and the best-performing ensemble multiclass classifier achieved a mean F4 score of 0.788 (0.773, 0.803).CONCLUSIONS: This work builds upon previous demonstrations of the utility of ML-derived decision support tools in clinical biochemistry laboratories. Our findings suggest that, pending additional validation studies, such tools could potentially be used in routine clinical practice to streamline and aid the interpretation of PAA profiles. This would be particularly useful in laboratories with limited resources and large workloads. We provide the necessary code for other laboratories to develop their own decision support tools.