Development of an equation to predict delta bilirubin levels using machine learning

IF 3.2 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
Saejin Lee , Kwangjin Ahn , Taesic Lee , Jooyoung Cho , Moon Young Kim , Young Uh
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引用次数: 0

Abstract

Objective

Delta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories.

Methods

A total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times.

Results

The selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv − 0.23 × direct bilirubin − 0.05 × hemoglobin − 0.04 × albumin + 0.10.

Conclusion

The equation established in this study is practical and can be easily applied in real-time in clinical laboratories.

利用机器学习建立预测δ胆红素水平的方程
目的δ胆红素(与白蛋白共价结合的胆红素)在鉴别肝脏排泄共轭胆红素功能受损方面具有重要的临床作用,但临床实验室无法对其进行实时测量以用于诊断。方法共收集 210 份样本,使用高效液相色谱法对其δ胆红素水平进行了四次测量。收集的数据包括年龄、性别、诊断代码、δ胆红素、总胆红素、直接胆红素、总蛋白、白蛋白、球蛋白、天门冬氨酸氨基转移酶、丙氨酸转氨酶、碱性磷酸酶、γ-谷氨酰转移酶、乳酸脱氢酶、血红蛋白、血清溶血值、溶血指数、黄疸值(Iv)、黄疸指数(Ii)、脂血值(Lv)和脂血指数。为了进行特征选择并确定变量的最佳组合,进行了 1,000 次线性回归机器学习。结果所选变量为总胆红素、直接胆红素、总蛋白、白蛋白、血红蛋白、Iv、Ii 和 Lv。由这些变量组成的最终方程如下:δ胆红素 = 0.35 × Iv + 0.05 × Lv - 0.23 × 直接胆红素 - 0.05 × 血红蛋白 - 0.04 × 白蛋白 + 0.10。
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来源期刊
Clinica Chimica Acta
Clinica Chimica Acta 医学-医学实验技术
CiteScore
10.10
自引率
2.00%
发文量
1268
审稿时长
23 days
期刊介绍: The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.
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