A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews
A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews
复制标题
DOI：
10.1136/amiajnl-2014-002711
复制
发表时间：
2014-11-01
影响因子：
6.4
通讯作者：
Dredze, Mark
中科院分区：
文献类型：
--
作者：
Wallace, Byron C.;Paul, Michael J.;Dredze, Mark
在线医生评论是一个巨大的和潜在的丰富的信息来源，捕捉病人的情绪有关医疗保健。我们分析了一个语料库，包括近60 000这样的评论与国家的最先进的概率模型的文本。我们描述了一个概率生成模型，捕捉潜在的情感在各个方面的护理（如，人际关系的方式）。我们通过利用一小组手动注释的评论来定位特定的方面。我们进行回归分析，以评估模型输出是否改善了与州一级医疗保健措施的相关性。我们报告定性和定量结果。模型输出与州一级的医疗质量指标相关，包括患者在出院后14天内拜访其初级保健医生的可能性（p=0.03），并且使用所提出的模型可以更好地预测这一结果（p= 0.10）。我们在医疗保健支出方面也发现了类似的结果。文本的生成模型可以从在线医生评论中恢复重要信息，促进对此类评论的大规模分析。
Online physician reviews are a massive and potentially rich source of information capturing patient sentiment regarding healthcare. We analyze a corpus comprising nearly 60 000 such reviews with a state-of-the-art probabilistic model of text. We describe a probabilistic generative model that captures latent sentiment across aspects of care (eg, interpersonal manner). We target specific aspects by leveraging a small set of manually annotated reviews. We perform regression analysis to assess whether model output improves correlation with state-level measures of healthcare. We report both qualitative and quantitative results. Model output correlates with state-level measures of quality healthcare, including patient likelihood of visiting their primary care physician within 14 days of discharge (p=0.03), and using the proposed model better predicts this outcome (p= 0.10). We find similar results for healthcare expenditure. Generative models of text can recover important information from online physician reviews, facilitating large-scale analyses of such reviews.