调研医生专家选实体识别工具的痛点与标准
Survey: Understand the challenges of MachineLearning Experts using Named EntityRecognition Tools
- 基于问卷调研ML专家评估命名实体识别工具的标准
- 发现专家选型时面临工具性能、可解释性等核心挑战
- 适合医疗AI开发者和工具设计者参考
本文采用Kasunic的调研方法，系统分析机器学习专家在选择命名实体识别（NER）工具和框架时所依据的评价标准。由于在临床指南开发中利用NER支持信息检索至关重要，因此工具的比较与选型成为关键环节。研究结合Nunamaker的方法论，首先介绍研究背景，梳理技术现状，识别专家调研中的挑战，并详细说明调研设计与实施过程。最终对调研结果进行评估，提炼出重要洞见，总结结论。
原文摘要 · Abstract (English)
This paper presents a survey based on Kasunic's survey research methodology to identify the criteria used by Machine Learning (ML) experts to evaluate Named Entity Recognition (NER) tools and frameworks. Comparison and selection of NER tools and frameworks is a critical step in leveraging NER for Information Retrieval to support the development of Clinical Practice Guidelines. In addition, this study examines the main challenges faced by ML experts when choosing suitable NER tools and frameworks. Using Nunamaker's methodology, the article begins with an introduction to the topic, contextualizes the research, reviews the state-of-the-art in science and technology, and identifies challenges for an expert survey on NER tools and frameworks. This is followed by a description of the survey's design and implementation. The paper concludes with an evaluation of the survey results and the insights gained, ending with a summary and conclusions.
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