JinhuaNSICU, an open accessible Neurosurgical Intensive Care Database.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-09 DOI:10.1038/s41597-025-05915-8

Lin Chen, Ziyi Huang, Xiaoli Liu, Guodong He, Binbin Ren, Fan Yang, Fengfeng Jiang, Qi Tu, Dandan Cai, Kai Zhang, Zhongheng Zhang, Gensheng Zhang, Minfeng Tong

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引用次数: 0

Abstract

The Jinhua Central Hospital Neurosurgical Intensive Care Unit Database (JinhuaNSICU) is a single-center, bilingual (Chinese and English), and open public database focused on neurosurgery patients. This database has been filtered, cleaned, and de-identified, recording a variety of rich clinical information throughout the entire hospital stay of patients admitted to the NSICU. It contains 8,773 individuals with 20,530 episodes' demographics, laboratory tests, vital signs, microbiology culture, examinations with reports, medications with extra special chinese herbal, orders, operations, and surgery with anesthesia. More than 26% of patients have records of more than three hospitalizations. Creating this neurosurgery database will bridge the existing gap in neurosurgical data and inspire more researchers to participate in neurosurgical research, which will further promote the follow-up studies, development of clinical decision support tools and sharing of data for international observation analysis.

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JinhuaNSICU，开放的神经外科重症监护数据库。

金华市中心医院神经外科重症监护病房数据库（JinhuaNSICU）是一个以神经外科患者为重点的单中心、中英文双语、开放的公共数据库。该数据库经过过滤、清理和去识别，记录了NSICU住院患者整个住院期间的各种丰富的临床信息。它包含8,773个人的20,530例人口统计、实验室检查、生命体征、微生物培养、检查报告、特殊中药药物、医嘱、手术和麻醉手术。超过26%的患者有三次以上的住院记录。建立该神经外科数据库将弥补现有神经外科数据的空白，激励更多的研究人员参与神经外科研究，进一步促进随访研究、临床决策支持工具的开发以及国际观察分析数据的共享。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.