Developing Embedded Taxonomy and Mining Patients' Interests From Web-Based Physician Reviews: Mixed-Methods Approach.
Developing Embedded Taxonomy and Mining Patients' Interests From Web-Based Physician Reviews: Mixed-Methods Approach.
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开发嵌入式分类法并从基于网络的医生评论中挖掘患者的兴趣：混合方法
DOI：
10.2196/jmir.8868
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发表时间：
2018-08-16
影响因子：
7.4
通讯作者：
Liu J
中科院分区：
文献类型：
--
作者：
Li J;Liu M;Li X;Liu X;Liu J
基于网络的医生评论是值得进一步研究的无价之宝。虽然许多研究探索了医生评论的文本信息，但很少有人专注于开发嵌入医生评论的系统主题分类法。矿业医生评审的第一步是确定自然结构或维度是如何嵌入评审中的。因此，严谨、系统地发展主题分类法具有重要意义。本研究旨在建立一种层次化的主题分类法，以揭示医生评论的潜在结构，并说明其在挖掘患者兴趣方面的应用。数据包括122,716篇医生评论，其中包括2007年至2015年收集的中国(haodf.com)一家领先的医生评论网站上的8,501名医生的评论。采用文献综述、基于数据驱动的主题发现和人工标注相结合的方法，建立了医师评审主题分类法。已确定的分类包括3个域或高级类别和9个子主题或低级别类别。与医生相关的领域包括医德、医疗能力、沟通技能、医疗建议和处方类别。与患者相关的领域包括患者概况、症状、诊断和发病机制的类别。与系统相关的领域包括融资类别和操作流程。提出的分类算法的F-度量平均达到0.816。急症患者更常提到症状(Cohen d=1.58，Δu=0.216，t=229.75，P<；.001)，而沟通能力(Cohen d=−0.038，Δu=−0.038，t=−42.01，P<；.001)、财务(Cohen d=−0.68，Δu=−0.098，t=−99.26，P<；.001)和诊断与发病(Cohen d=−0.55，Δu=−0.078，t=−80.09，P<；.001)；.001)是慢性病患者更常提到的。轻症患者对医德(Cohen d=0.2 5，Δu 0.039，t=8.33，P<；0.0 0 1)、手术过程(Cohen d=0.5 7，Δu 0.060，t=18.75，P<；0.0 0 1)、患者概况(Cohen d=1.19，Δu 0.132，t=3 9.33，P<；0.0 0 1)和症状(Cohen d=1.91，Δu=0.274，t=62.82，P<；0.001)更感兴趣。同时，严重疾病患者对医疗能力(Cohen d=−0.99，Δu=−0.165，t=−32.58，P<；.001)、医疗建议和处方(Cohen d=−0.65，Δu=−0.082，t=−21.45，P<；.001)、财务(Cohen d=−0.26，Δu=−0.018，t=−8.45，P<；.001)、财务(Cohen d=−0.26，Δu=−0.018，t=−8.45，P<；.001)更感兴趣。诊断和发病机制(Cohen d=−1.55，Δu=−0.229，t=−50.93，P<；001)。这种混合方法集成了文献综述、数据驱动的主题发现和人工注释，是开发医生综述主题分类法的一种有效和严格的方法。提出的基于标签-潜在Dirichlet分配的分类算法能够获得令人印象深刻的分类结果，用于挖掘患者的兴趣。此外，挖掘结果显示，不同疾病类型、社会经济发展水平和医院级别的患者兴趣存在显著差异。
Web-based physician reviews are invaluable gold mines that merit further investigation. Although many studies have explored the text information of physician reviews, very few have focused on developing a systematic topic taxonomy embedded in physician reviews. The first step toward mining physician reviews is to determine how the natural structure or dimensions is embedded in reviews. Therefore, it is relevant to develop the topic taxonomy rigorously and systematically. This study aims to develop a hierarchical topic taxonomy to uncover the latent structure of physician reviews and illustrate its application for mining patients’ interests based on the proposed taxonomy and algorithm. Data comprised 122,716 physician reviews, including reviews of 8501 doctors from a leading physician review website in China (haodf.com), collected between 2007 and 2015. Mixed methods, including a literature review, data-driven-based topic discovery, and human annotation were used to develop the physician review topic taxonomy. The identified taxonomy included 3 domains or high-level categories and 9 subtopics or low-level categories. The physician-related domain included the categories of medical ethics, medical competence, communication skills, medical advice, and prescriptions. The patient-related domain included the categories of the patient profile, symptoms, diagnosis, and pathogenesis. The system-related domain included the categories of financing and operation process. The F-measure of the proposed classification algorithm reached 0.816 on average. Symptoms (Cohen d=1.58, Δu=0.216, t=229.75, and P<.001) are more often mentioned by patients with acute diseases, whereas communication skills (Cohen d=−0.29, Δu=−0.038, t=−42.01, and P<.001), financing (Cohen d=−0.68, Δu=−0.098, t=−99.26, and P<.001), and diagnosis and pathogenesis (Cohen d=−0.55, Δu=−0.078, t=−80.09, and P<.001) are more often mentioned by patients with chronic diseases. Patients with mild diseases were more interested in medical ethics (Cohen d=0.25, Δu 0.039, t=8.33, and P<.001), operation process (Cohen d=0.57, Δu 0.060, t=18.75, and P<.001), patient profile (Cohen d=1.19, Δu 0.132, t=39.33, and P<.001), and symptoms (Cohen d=1.91, Δu=0.274, t=62.82, and P<.001). Meanwhile, patients with serious diseases were more interested in medical competence (Cohen d=−0.99, Δu=−0.165, t=−32.58, and P<.001), medical advice and prescription (Cohen d=−0.65, Δu=−0.082, t=−21.45, and P<.001), financing (Cohen d=−0.26, Δu=−0.018, t=−8.45, and P<.001), and diagnosis and pathogenesis (Cohen d=−1.55, Δu=−0.229, t=−50.93, and P<.001). This mixed-methods approach, integrating literature reviews, data-driven topic discovery, and human annotation, is an effective and rigorous way to develop a physician review topic taxonomy. The proposed algorithm based on Labeled-Latent Dirichlet Allocation can achieve impressive classification results for mining patients’ interests. Furthermore, the mining results reveal marked differences in patients’ interests across different disease types, socioeconomic development levels, and hospital levels.
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作者：
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