Using Machine Learning to Predict Laboratory Test Results
Using Machine Learning to Predict Laboratory Test Results
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DOI：
10.1093/ajcp/aqw064
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发表时间：
2016-06-01
影响因子：
3.5
通讯作者：
Baron, Jason M.
中科院分区：
文献类型：
--
作者：
Luo, Yuan;Szolovits, Peter;Baron, Jason M.
目的：虽然临床实验室报告的大多数测试结果作为个人的数字，发现，或观察，临床诊断通常依赖于多个测试的结果。临床决策支持，集成多个元素的实验室数据可能是非常有用的，在提高实验室diagnosis.Methods：使用分析物铁蛋白的概念证明，我们提取临床实验室数据从病人的测试和应用各种机器学习算法来预测铁蛋白测试结果使用其他测试的结果。我们比较预测与测量结果和审查选定的情况下，以评估预测的ferritin.Results的临床价值：我们表明，患者的人口统计学和其他实验室检查的结果可以区分正常的异常铁蛋白的结果具有很高的准确度（曲线下面积高达0.97，举行了测试数据）。病例回顾表明，预测的铁蛋白结果有时可能更好地反映潜在的铁状态比测量ferritin.Conclusions：这些研究结果突出了大量的信息冗余存在于患者的测试结果，并提供了一个潜在的基础，一种新型的临床决策支持，旨在整合，解释，并提高临床实验室检测结果的多分析物集的诊断价值。
Objectives: While clinical laboratories report most test results as individual numbers, findings, or observations, clinical diagnosis usually relies on the results of multiple tests. Clinical decision support that integrates multiple elements of laboratory data could be highly useful in enhancing laboratory diagnosis.Methods: Using the analyte ferritin in a proof of concept, we extracted clinical laboratory data from patient testing and applied a variety of machine-learning algorithms to predict ferritin test results using the results from other tests. We compared predicted with measured results and reviewed selected cases to assess the clinical value of predicted ferritin.Results: We show that patient demographics and results of other laboratory tests can discriminate normal from abnormal ferritin results with a high degree of accuracy (area under the curve as high as 0.97, held-out test data). Case review indicated that predicted ferritin results may sometimes better reflect underlying iron status than measured ferritin.Conclusions: These findings highlight the substantial informational redundancy present in patient test results and offer a potential foundation for a novel type of clinical decision support aimed at integrating, interpreting, and enhancing the diagnostic value of multianalyte sets of clinical laboratory test results.