Luo Yuan, Szolovits Peter, Dighe Anand S, Baron Jason M
From the Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge.
Department of Pathology, Massachusetts General Hospital, Boston Harvard Medical School, Boston, MA.
Am J Clin Pathol. 2016 Jun;145(6):778-88. doi: 10.1093/ajcp/aqw064. Epub 2016 Jun 21.
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.
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.
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.
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.
虽然临床实验室将大多数检测结果报告为单个数字、发现或观察结果，但临床诊断通常依赖于多项检测的结果。整合实验室数据多个要素的临床决策支持对于加强实验室诊断可能非常有用。
在一个概念验证中，我们以铁蛋白作为分析物，从患者检测中提取临床实验室数据，并应用多种机器学习算法，利用其他检测结果预测铁蛋白检测结果。我们将预测结果与测量结果进行比较，并审查选定病例以评估预测铁蛋白的临床价值。
我们表明，患者人口统计学特征和其他实验室检测结果能够以高度准确性区分正常与异常铁蛋白结果（曲线下面积高达0.97，外部测试数据）。病例审查表明，预测的铁蛋白结果有时可能比测量的铁蛋白结果更好地反映潜在的铁状态。
这些发现突出了患者检测结果中存在的大量信息冗余，并为一种新型临床决策支持提供了潜在基础，该支持旨在整合、解释和提高临床实验室检测结果多分析物集的诊断价值。
