开发一个预测模型来预测技术融合:一个基于变压器的模型和监督学习方法
Mokh Afifuddin1,2, Wonchul Seo2
1Textile Community College of Surakarta, Surakarta, Central Java, Indonesia.
PloS one
|June 26, 2025
概括
这项研究引入了一种新方法,利用文本挖掘和机器学习来预测生物医疗技术的融合. 它确定了新兴趋势和合作机会,以促进业务增长.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 技术管理 技术管理
背景情况:
- 技术的融合正在迅速改变生物医疗保健领域.
- 预测这些融合趋势对于创新和竞争优势至关重要.
- 现有的方法往往缺乏捕捉复杂的相互关系的复杂性.
研究的目的:
- 开发一种强大的方法来预测生物医疗保健领域的技术融合.
- 利用文本挖掘和监督学习来识别新兴趋势.
- 为跨学科合作和商业战略提供可操作的见解.
主要方法:
- 利用变压器模型 (例如,BERTopic与KeyBERT,OpenAI) 来从专利和研究文章中提取技术主题.
- 集成技术相似性,链接预测和因果关系作为预测模型的输入特征.
- 采用机器学习和深度学习技术来训练技术融合的预测模型.
主要成果:
- 在生物医疗保健领域成功确定了潜在的技术融合机会.
- 揭示了新兴趋势和技术主题之间的复杂关系.
- 证明了拟议的综合方法在预测趋同方面的有效性.
结论:
- 该研究提供了一种新的方法来预测生物医疗保健领域未来的技术融合.
- 调查结果为确定跨学科合作机会提供了基础.
- 这种方法对企业有重大影响,企业的目标是通过创新实现可持续增长.
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