使用自然语言处理来识别儿科瘤学中的症状不良事件:临床研究人员的教程
Clifton P Thornton1,2, Maryam Daniali3, Lei Wang3,4
1Center for Pediatric Nursing Research & Evidence-Based Practice, Children's Hospital of Philadelphia, 734 Schuylkill Avenue, Philadelphia, PA, 19104, United States, 1 2674266188.
JMIR bioinformatics and biotechnology
|December 4, 2025
概括
本教程指导临床医生将人工智能 (AI) 整合到瘤学研究中. 它详细介绍了开发人工智能模型的步骤,并强调了跨学科合作和机构支持对在医疗保健中成功实施人工智能的重要性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 为推进医疗保健研究和提供提供提供了巨大的潜力.
- 临床医生经常面临人工智能整合的挑战,原因是缺乏培训,技术障碍和机构约束.
研究的目的:
- 提供开发人工智能模型用于识别儿科癌症治疗中的不良事件的透明说明.
- 为有兴趣在研究和实践中利用AI的临床医生概述可行的步骤.
- 为瘤学临床医生解密人工智能研究过程.
主要方法:
- 形成由临床瘤学研究人员和数据科学家组成的跨学科团队.
- 开发一种自然语言处理 (NLP) 模型来识别症状性不良事件.
- 策划一个大型的临床数据集和建立一个数字实验室.
主要成果:
- 这个过程涉及克服跨学科合作中的学习曲线,财务和时间限制.
- 早期人工智能模型的成功开发是通过专门的团队合作和机构支持来实现的.
- 确定了在临床环境中实施人工智能研究的促进者和障碍.
结论:
- 临床医生可以通过建立数字实验室,策划数据和开发AI模型来启动人工智能研究.
- 强有力的机构支持和与数据科学家的合作伙伴关系对于人工智能研究的成功至关重要.
- 分享经验对于在瘤学研究和实践中导航AI不断变化的景观至关重要.
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