Suppr超能文献

在临床记录中识别补充剂的使用:自然语言处理的应用

Identifying Supplement Use Within Clinical Notes: An Applicationof Natural Language Processing.

作者信息

Sharma Vivekanand, Sarkar Indra Neil

机构信息

Center for Biomedical Informatics, Brown University, Providence, Rhode Island.

出版信息

AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:196-205. eCollection 2018.

Abstract

Recent statistics indicate that the use of dietary supplements has increased over the years. Although being popular among consumers who use them for a variety of reasons, there have been limited clinical data-driven studies of the impact of dietary supplements on health outcomes. Challenges that impede such analyses in a comprehensive manner include either the sequestered nature of such data or their embedding within biomedical and clinical text. This study explored the feasibility to uncover patterns in the use of supplements, focusing on vitamin use among patients diagnosed with mental illness within patient records from the MIMIC-III database. The relevance of vitamin(s) was calculated at different levels of granularity and compared with association identified from Dietary Supplement Subset of MEDLINE. The results reveal insights into vitamin use for specific mental health related diagnosis and highlight challenges with identifying supplement information from clinical sources.

摘要

最近的统计数据表明,多年来膳食补充剂的使用有所增加。尽管它们在因各种原因使用的消费者中很受欢迎,但关于膳食补充剂对健康结果影响的临床数据驱动研究却很有限。以全面方式阻碍此类分析的挑战包括此类数据的隐秘性质或它们嵌入生物医学和临床文本之中。本研究探讨了在补充剂使用中发现模式的可行性,重点关注从MIMIC-III数据库的患者记录中诊断出患有精神疾病的患者的维生素使用情况。在不同粒度水平上计算了维生素的相关性,并与从MEDLINE的膳食补充剂子集中确定的关联进行了比较。结果揭示了针对特定心理健康相关诊断的维生素使用情况的见解,并突出了从临床来源识别补充剂信息的挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d60/5961809/eecba4109028/2840041f1.jpg

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