Symptom clusters in women with breast cancer: an analysis of data from social media and a research study.
Symptom clusters in women with breast cancer: an analysis of data from social media and a research study.
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DOI：
10.1007/s11136-015-1156-7
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发表时间：
2016-03
影响因子：
3.5
通讯作者：
Ip, Edward H.
中科院分区：
文献类型：
--
作者：
Marshall, Sarah A.;Yang, Christopher C.;Ping, Qing;Zhao, Mengnan;Avis, Nancy E.;Ip, Edward H.
社交媒体网站上的用户生成内容，如与健康相关的在线论坛，为研究人员提供了大量诱人的信息，但对这些数据的科学应用的担忧仍然存在。本文比较和对比的症状集群模式来自乳腺癌论坛上的信息与那些从乳腺癌幸存者参加研究完成的症状检查表。研究人员将MedHelp.org上乳腺癌论坛的12 991名用户生成的50 000多条信息转换成标准表格，并检查了25种症状的共同出现情况。使用k-中心点聚类法确定症状在聚类中的适当位置。研究结果与一项类似的分析进行了比较，该分析对653名参与研究的乳腺癌幸存者进行了症状检查表。使用论坛数据确定了以下聚类：绝经期/心理、疼痛/疲劳、胃肠道和其他。研究数据产生了以下集群：更年期、疼痛、疲劳/睡眠/胃肠道、心理和体重/食欲增加。虽然这些集群有些不同，但在社交媒体分析中聚集在一起的许多症状在研究参与者的分析中仍然在一起。症状之间的联系密度，如共同出现率和相似性所反映的，在研究数据中较高。社交媒体产生的大量数据可以增强传统数据源的发现。当不同的信息来源结合在一起时，可以检测到重叠和差异的区域，也许可以让研究人员更准确地了解现实。但是，必须谨慎使用来自社交媒体的数据，并了解其局限性。
User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study. Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study. The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data. The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations.
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药物不良事件的文本挖掘：前景、挑战和最新技术。
影响因子：
4.2
作者：
Harpaz, Rave;Callahan, Alison;Tamang, Suzanne;Low, Yen;Odgers, David;Finlayson, Sam;Jung, Kenneth;LePendu, Paea;Shah, Nigam H.
通讯作者：
Shah, Nigam H.
DOI：
10.1007/s11764-009-0100-7
发表时间：
2009-12
期刊：
Journal of cancer survivorship : research and practice
影响因子：
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作者：
Fu OS;Crew KD;Jacobson JS;Greenlee H;Yu G;Campbell J;Ortiz Y;Hershman DL
通讯作者：
Hershman DL
影响因子：
7.4
作者：
Alshaikh, Fahdah;Ramzan, Farzan;Majeed, Azeem
通讯作者：
Majeed, Azeem
影响因子：
3.8
作者：
Avis, Nancy E.;Levine, Beverly;Naughton, Michelle J.;Case, L. Douglas;Naftalis, Elizabeth;Van Zee, Kimberly J.
通讯作者：
Van Zee, Kimberly J.
DOI：
10.1046/j.1523-5394.1999.75008.x
发表时间：
1999-09-01
期刊：
CANCER PRACTICE
影响因子：
--
作者：
Gaston-Johansson, F;Fall-Dickson, JM;Kennedy, MJ
通讯作者：
Kennedy, MJ