Use of sentiment analysis for capturing patient experience from free-text comments posted online.
Use of sentiment analysis for capturing patient experience from free-text comments posted online.
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DOI：
10.2196/jmir.2721
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发表时间：
2013-11-01
影响因子：
7.4
通讯作者：
Donaldson L
中科院分区：
文献类型：
--
作者：
Greaves F;Ramirez-Cano D;Millett C;Darzi A;Donaldson L
在互联网上的博客、社交网络和医生评级网站上，有大量关于医疗保健质量的非结构化、自由文本信息，这些信息没有以系统的方式获取。新的分析技术，如情感分析，可以让我们更有效地理解和使用这些信息，以提高医疗保健的质量。我们试图使用机器学习来理解患者对他们的护理的非结构化评论。我们使用情感分析技术将患者的在线自由文本评论分类为对其医疗保健的积极或消极描述。我们试图自动预测患者是否会推荐医院，医院是否干净，以及他们是否从他们的自由文本描述中得到有尊严的治疗，与患者自己对他们的护理的量化评级相比。我们使用Weka数据挖掘软件，将机器学习技术应用于2010年英国国家卫生服务网站上所有6412条关于医院的在线评论。我们还比较了情绪分析的结果与基于纸张的全国住院病人调查结果在医院一级使用斯皮尔曼等级相关的所有161急性成人医院在英格兰的信任。护理的定量评分与使用情感分析的自由文本评论对清洁度、尊严治疗和医院总体推荐的评分之间分别有81%、84%和89%的一致性（Kappa评分：0.40 - 0.74，P<0.001）。我们观察到我们的机器学习预测与对检查的三个类别的大型患者调查的反应之间存在轻度到中度的相关性（斯皮尔曼rho 0.37-0.51，所有P<0.001）。我们使用这种机器学习过程实现的预测准确性表明，我们能够从自由文本中预测患者对医院不同性能方面的意见的合理准确评估，并且这些机器学习预测与更传统的调查结果相关联。
There are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. We attempted to use machine learning to understand patients’ unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient’s own quantitative rating of their care. We applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. There was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40–.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all). The prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients’ opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys.
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影响因子：
1.2
作者：
Alemi, Farrokh;Torii, Manabu;Aron, David C
通讯作者：
Aron, David C
医师质量报告的不断变化的景观：分析患者在5年内对医师的在线评级。
影响因子：
7.4
作者：
Gao GG;McCullough JS;Agarwal R;Jha AK
通讯作者：
Jha AK
影响因子：
5.8
作者：
Frank, E;Hall, M;Witten, IH
通讯作者：
Witten, IH
影响因子：
7.4
作者：
Greaves F;Pape UJ;Lee H;Smith DM;Darzi A;Majeed A;Millett C
通讯作者：
Millett C
DOI：
10.1016/s1553-7250(13)39003-5
发表时间：
2013-01-01
影响因子：
2.3
作者：
Lagu, Tara;Goff, Sarah L.;Lindenauer, Peter K.
通讯作者：
Lindenauer, Peter K.