了解癌症幸存者护理需求使用亚马逊评论:内容分析,算法开发和验证研究
Liwei Wang1, Qiuhao Lu2, Rui Li2
1Department of Clinical and Health Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX, 77030, United States, 1 713-500-3900.
JMIR cancer
|September 23, 2025
概括
亚马逊评论为癌症幸存者护理需求提供了宝贵的见解,特别是症状自我管理. 这项研究提出了一个新的数据集和基线模型来分析这些真实世界的数据,以改善患者的支持.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
背景情况:
- 癌症幸存者越来越多地使用补充疗法.
- 亚马逊评论是关于患者体验和需求的现实数据的丰富来源.
- 了解幸存者护理需求对于改善患者的治疗结果至关重要.
研究的目的:
- 探索亚马逊消费者评论的潜力,以确定癌症幸存者的护理需求.
- 为了研究,从亚马逊评论中开发一个手动注释的语料库.
- 为分析这些数据建立基准自然语言处理 (NLP) 模型.
主要方法:
- 预处理亚马逊评论以识别与癌症有关的句子.
- 进行内容分析,包括主题建模和情绪分析.
- 制定注释准则,并为命名实体识别和文本分类创建语料库.
主要成果:
- 从3349个与癌症相关的评论中确定了4703个句子.
- 主题建模揭示了对症状管理和生存经验的洞察力.
- 基线模型实现了F1得分,最高为NER的66.92%和文本分类的88.46%.
结论:
- 亚马逊评论是了解癌症幸存者的需求和自我管理策略的可行数据来源.
- 创建的集体和基线模型支持未来癌症存活率研究.
- 这种方法可以为临床指导方针提供信息,并改善对癌症幸存者的支持.
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