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评估乳腺癌幸存者未满足的信息需求:使用文本分类和检索对在线健康论坛进行的探索性研究。

Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval.

作者信息

McRoy Susan, Rastegar-Mojarad Majid, Wang Yanshan, Ruddy Kathryn J, Haddad Tufia C, Liu Hongfang

机构信息

Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.

Mayo Clinic, Rochester, MN, United States.

出版信息

JMIR Cancer. 2018 May 15;4(1):e10. doi: 10.2196/cancer.9050.

Abstract

BACKGROUND

Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources.

OBJECTIVE

The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials.

METHODS

This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval.

RESULTS

We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one.

CONCLUSIONS

Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most a third of breast cancer survivors' questions would be addressed by the materials currently provided to them.

摘要

背景

提供给乳腺癌幸存者的患者教育材料可能并不完全符合他们的信息需求。由于各种原因,需求可能会随时间变化、被遗忘或被错误报告。对幸存者在在线健康论坛上发布的内容进行自动内容分析,可以确定一段时间内表达的信息需求,并以低成本定期重复进行。识别这些未满足的需求可以指导对现有教育材料的改进以及新资源的创建。

目的

本项目的主要目标是从乳腺癌幸存者自身的角度评估未满足的信息需求,并确定信息需求与当前教育材料之间的差距。

方法

该方法采用计算方法进行内容建模和监督文本分类,对来自在线健康论坛的数据进行分析,以识别对健康相关信息的明确和隐含请求。使用信息检索技术确定需求与教育材料之间的潜在差距。

结果

我们为在线健康论坛数据中的句子分类提供了一种新的分类法。从两个在线健康论坛中选择了260个帖子,产生了4179个句子用于编码。在对数据进行注释并训练替代的一对多分类器后,基于随机森林的方法在主要信息类型上的F1分数从66%(其他,数据集2)到90%(医学,数据集1)。使用136个需求表达来生成对索引教育材料的查询。在检查每个查询检索到的最佳两页时,发现12%(17/136)的查询被所有编码人员判定有相关内容,33%(45/136)的查询被至少一名编码人员判定有相关内容。

结论

可以使用自动化方法有效地分析在线健康论坛的文本。我们的分析证实,乳腺癌幸存者有许多信息需求未被他们通常收到的书面文件涵盖,因为我们的结果表明,目前提供给他们的材料最多只能解决三分之一的乳腺癌幸存者的问题。

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