使用机器学习算法自动预测低铁蛋白浓度

S. Kurstjens, Thomas de Bel, A. van der Horst, R. Kusters, J. Krabbe, J. V. van Balveren
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引用次数: 10

摘要

目的实验室检测结果解释的计算算法可以为检验医学的医生和专家提供支持。本研究的目的是开发、实施和评估一种机器学习算法,该算法使用最小的一组基本实验室测试,即全血细胞计数和c反应蛋白(CRP),自动评估贫血初级保健患者体内铁储量低的风险(由低铁蛋白血浆水平反映)。方法利用贫血初级保健患者的实验室测量数据来开发和验证机器学习算法。该算法的性能与来自三家大型教学医院的12名检验医学专家进行了比较,这些专家根据实验室检测报告(全血细胞计数和CRP)预测贫血患者是否有低铁蛋白水平。在第二轮评估中,算法结果作为决策支持工具提供给检验医学专家。结果基于两种不同的化学分析仪建立了两种不同的低铁蛋白浓度预测算法,ROC曲线下面积分别为0.92(西门子)和0.90(罗氏)。与算法相比,实验室医学专家在预测低铁蛋白浓度方面的准确性较低,即使知道算法作为支持工具的输出。在实验室系统中实施该算法,平均每天新增1例缺铁诊断。结论基于常规实验室检测结果的机器学习算法可准确预测贫血患者低铁蛋白水平。此外,该算法在实验室系统中的实施减少了未被识别的铁缺乏症的数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated prediction of low ferritin concentrations using a machine learning algorithm
Abstract Objectives Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP). Methods Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool. Results Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day. Conclusions Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.
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