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何时重新订购实验室检测?了解实验室检测的有效期。

When to re-order laboratory tests? Learning laboratory test shelf-life.

机构信息

Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA.

Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA.

出版信息

J Biomed Inform. 2018 Sep;85:21-29. doi: 10.1016/j.jbi.2018.07.019. Epub 2018 Jul 20.

Abstract

Most laboratory results are valid for only a certain time period (laboratory tests shelf-life), after which they are outdated and the test needs to be re-administered. Currently, laboratory test shelf-lives are not centrally available anywhere but the implicit knowledge of doctors. In this work we propose an automated method to learn laboratory test-specific shelf-life by identifying prevalent laboratory test order patterns in electronic health records. The resulting shelf-lives performed well in the evaluation of internal validity, clinical interpretability, and external validity.

摘要

大多数实验室结果的有效期仅为一段时间(实验室测试保质期),之后结果就会过期,需要重新进行测试。目前,实验室测试保质期并没有集中在任何地方,而只是医生的隐性知识。在这项工作中,我们通过识别电子健康记录中常见的实验室测试订单模式,提出了一种自动学习实验室测试特定保质期的方法。所得到的保质期在内部有效性、临床可解释性和外部有效性的评估中表现良好。

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本文引用的文献

1
Using statistical anomaly detection models to find clinical decision support malfunctions.
J Am Med Inform Assoc. 2018 Jul 1;25(7):862-871. doi: 10.1093/jamia/ocy041.
2
Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.
Int J Med Inform. 2017 Jun;102:71-79. doi: 10.1016/j.ijmedinf.2017.03.006. Epub 2017 Mar 18.
4
Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.
J Am Med Inform Assoc. 2017 May 1;24(3):472-480. doi: 10.1093/jamia/ocw136.
5
Anomaly Detection in Host Signaling Pathways for the Early Prognosis of Acute Infection.
PLoS One. 2016 Aug 17;11(8):e0160919. doi: 10.1371/journal.pone.0160919. eCollection 2016.
6
An unsupervised learning method to identify reference intervals from a clinical database.
J Biomed Inform. 2016 Feb;59:276-84. doi: 10.1016/j.jbi.2015.12.010. Epub 2015 Dec 19.
7
Learning probabilistic phenotypes from heterogeneous EHR data.
J Biomed Inform. 2015 Dec;58:156-165. doi: 10.1016/j.jbi.2015.10.001. Epub 2015 Oct 14.
8
OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.
J Am Med Inform Assoc. 2016 Mar;23(2):339-48. doi: 10.1093/jamia/ocv091. Epub 2015 Jul 21.
9
Intercepting wrong-patient orders in a computerized provider order entry system.
Ann Emerg Med. 2015 Jun;65(6):679-686.e1. doi: 10.1016/j.annemergmed.2014.11.017. Epub 2014 Dec 18.
10
Temporal trends of hemoglobin A1c testing.
J Am Med Inform Assoc. 2014 Nov-Dec;21(6):1038-44. doi: 10.1136/amiajnl-2013-002592. Epub 2014 Jun 13.

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