STEED:一种数据挖掘工具,用于从体内出版物中自动提取实验参数和偏差风险项目
Wolfgang Emanuel Zurrer1, Amelia Elaine Cannon1, Ewoud Ewing2
1Center for Reproducible Science, University of Zurich, Zurich, Switzerland.
PloS one
|November 26, 2024
概括
一个新的数据挖掘工具,STEED,自动提取神经科学 in vivo 研究中的数据,显著减少时间和提高系统审查效率. 这种工具提高了基于证据的医学研究的可行性.
科学领域:
- 神经科学是一个神经科学.
- 生物医学研究生物医学研究
- 基于证据的医学基于证据的医学.
背景情况:
- 系统性审查对于基于证据的医学至关重要,但由于劳动密集的数据提取,它面临着挑战.
- 越来越多的生物医学文献加剧了这些挑战,阻碍了系统审查的可行性.
研究的目的:
- 开发一个自动化数据挖掘工具,从神经科学 in vivo 出版物中提取信息.
- 在系统性审查中解决数据提取的时间密集性.
主要方法:
- 在 R 编程环境中开发了一个名为 STEED (结构化提取实验数据) 的数据挖掘工具.
- 在45个运动神经元疾病动物研究中训练了该工具,并在两个身体 (31个运动神经元疾病研究,244个多发性硬化症研究) 上验证了它.
主要成果:
- STEED成功地提取了关键的实验参数 (动物模型,物种) 和偏差项目风险 (随机化,盲目化).
- 实现了高性能指标:灵敏度>85%,特异性>80%,准确度>90%,F1得分>0.9在验证机构.
- 在数据提取方面节省了超过99%的时间.
结论:
- 该 STEED 文本挖掘工具有效地从神经科学 in vivo 文献中提取关键数据.
- STEED可以通过自动化数据提取来改善研究,大大节省时间并帮助系统审查.
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