来自先进医疗技术倡议提交的工程特征,以实现提案成功的预测建模
Holly Pavliscsak1, Benjamin Knisely2
1Telemedicine and Advanced Technology Research Center (TATRC) South, Fort Eisenhower, GA 30905, USA.
Military medicine
|August 20, 2024
概括
机器学习 (ML) 可以通过预测基于内容特征的评分来帮助评估军事医学研究建议. 这种方法旨在协助专家,减少偏见,并简化高级医疗技术倡议 (AMTI) 的审查过程.
科学领域:
- 军事医学 军事医学
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
背景情况:
- 美国陆军的先进医疗技术倡议 (AMTI) 通过提案驱动的过程为军事医学研究提供资金.
- 提案选择涉及严格的同行评审,评估创新,军事相关性,成功指标和投资回报率.
- 当前的审查流程耗时,可能会受到偏见的影响.
研究的目的:
- 探索人工智能和机器学习 (ML) 的可行性,以预测提案评级.
- 开发一种基于模型的方法,帮助人类专家评估研究建议.
- 确定与成功融资结果相关的关键提案特征.
主要方法:
- 进行文献审查以确定ML模型的潜在预测特征.
- 从2010年至2022年期间提交的824个AMTI提案中提取的特征.
- 功能包括请求的资金,字数,可读性得分,引用/合作伙伴数据和TF-IDF字向量.
主要成果:
- 在提案摘要和关键部分中确定了诸如"数据"",健康"",伤害"",设备"",治疗"和"技术"等高级关键词.
- 使用弗莱什尺度评估了提案文本可读性,发现大多数领域都需要大学毕业生阅读水平.
- 分析了引用和合作伙伴作为提案成功的指标.
结论:
- 这项初步研究是迈向基于机器学习的系统的第一步,以支持提案审查.
- 该项目旨在提供自动化的支持,减少审查员偏见,并简化AMTI的行政流程.
- 获得的见解将通过提高研究资金效率和公平性,使军事卫生系统受益.
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