用AI处理科研数据三例：植物名提取、药物信息抽取、众筹项目分类
Generative AI for Research Data Processing: Lessons Learnt From Three Use Cases
- 用Claude 3 Opus模型完成复杂数据任务，替代传统方法
- 在种子名录、医疗评估报告、众筹项目中实现高精度信息提取
- 提供AI适用性判断与结果优化策略，适合科研自动化场景
自2022年ChatGPT发布以来，生成式AI引发广泛关注，但其输出的准确性与一致性仍存疑。本研究探索生成式AI在科研数据处理中的应用，针对规则或传统机器学习难以解决的任务，采用生成式AI进行实践。通过三个案例验证：1）从植物园历史种子名录中提取植物物种名称；2）从欧盟健康技术评估机构发布的文件中提取药物名称、适应症、相对有效性、成本效益等数据点；3）为众筹平台Kickstarter上的项目分配行业代码。研究总结出如何判断生成式AI是否适用于特定数据任务，并提出提升结果准确性和一致性的方法，为科研数据自动化处理提供实用经验。
原文摘要 · Abstract (English)
There has been enormous interest in generative AI since ChatGPT was launched in 2022. However, there are concerns about the accuracy and consistency of the outputs of generative AI. We have carried out an exploratory study on the application of this new technology in research data processing. We identified tasks for which rule-based or traditional machine learning approaches were difficult to apply, and then performed these tasks using generative AI. We demonstrate the feasibility of using the generative AI model Claude 3 Opus in three research projects involving complex data processing tasks: 1) Information extraction: We extract plant species names from historical seedlists (catalogues of seeds) published by botanical gardens. 2) Natural language understanding: We extract certain data points (name of drug, name of health indication, relative effectiveness, cost-effectiveness, etc.) from documents published by Health Technology Assessment organisations in the EU. 3) Text classification: We assign industry codes to projects on the crowdfunding website Kickstarter. We share the lessons we learnt from these use cases: How to determine if generative AI is an appropriate tool for a given data processing task, and if so, how to maximise the accuracy and consistency of the results obtained.
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