5主题模型的主题解释
基于糖尿病视网膜病变患者通话转录文本和元数据,通过主题模型与分类模型预测患者入组护理管理服务。

基本信息
资源简介
5主题模型的主题解释
【数据集背景】
本数据集源站标题为《Topic interpretation for the 5-topic model.》。源站首次发布:2025-09-18。
【数据内容】
源站原始描述:Diabetic Retinopathy (DR) is a complication related to diabetes that can lead to vision impairment. To assist DR patients, a care management company provides a telephone-based principal care management (PCM) service, which includes care coaching and other services to reduce barriers to care for patients with DR. Despite its benefits, enrollment in the program is suboptimal. This study developed predictive models using call transcripts to investigate factors associated with patient enrollment in…
【数据结构与技术规格】
文件数 1 个,合计 9.73 KB。Table 5.xls:application/vnd.ms-excel,9.73 KB。数据集 DOI:10.1371/journal.pdig.0000992.t005。
【主题与分类】
源站学科分类:Molecular Biology、Ecology、Immunology、Biological Sciences not elsewhere classified、Developmental Biology、Cancer。主题标签:maintain longer intervals、investigate factors associated、auc values ranging、agent sentiment ).、determining whether patients、predicting patient enrollment、predict patient enrollment、includes care coaching、extracted topics discussed、enrollment agents play。
【适用方向】
文本类数据可用于医学自然语言处理,如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面:https://plos.figshare.com/articles/dataset/Topic_interpretation_for_the_5-topic_model_/30160679
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Rus, Annisa Marlin Masbar; Ivy, Julie S.; Chi, Min; Plyler, Mitchell; Wells-Gray, Elaine; Mayorga, Maria E. (2025). Topic interpretation for the 5-topic model.. PLOS Digital Health. Dataset
下载信息
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使用方式
数据集获取(Figshare)
- 源站标题:Topic interpretation for the 5-topic model.
- DOI:10.1371/journal.pdig.0000992.t005
- 发布日期:2025-09-18
- 文件数:1 个
- 文件总体积:9.73 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Topic_interpretation_for_the_5-topic_model_/30160679
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/58081070"
文件清单
Table 5.xls(9.73 KB)
文件总体积 9.73 KB(源站 API 实测)。
数据说明
Diabetic Retinopathy (DR) is a complication related to diabetes that can lead to vision impairment. To assist DR patients, a care management company provides a telephone-based principal care management (PCM) service, which includes care coaching and other services to reduce barriers to care for patients with DR. Despite its benefits, enrollment in the program is suboptimal. This study developed predictive models using call transcripts to investigate factors associated with patient enrollment in the PCM service. We analyzed transcripts of calls made during the enrollment process (prior to enrollment) and feature-engineered the call metadata (i.e., transcript length, number of calls, time between calls, customer and agent sentiment). In addition, we extracted topics discussed in the transcripts using Structural Topic Modeling (STM) and converted them into vector representations. Utilizing call metadata alongside topics, we developed three classification models (call metadata, topic-based, and topic+metadata) to predict patient enrollment, with the latter demonstrating superior performance. The topic+metadata classification model outperformed the other two models in distinguishing between patient enrollment and non-enrollment, with AUC values ranging from 0.81 to 0.99 across models using 3 to 15-topics. The findings suggest that proactively offering to schedule an appointment after the program benefits explanation leads to a higher odds of enrollment. When the scheduling portion of the conversation is not considered, agents should cover all parts of the script over multiple calls. Additionally, agents who explain the program and maintain longer intervals between calls have higher odds of patient enrollment, suggesting that there is value in allowing patients adequate time to reflect between calls. These findings offer valuable insights for agents to evaluate their strategies in patient enrollment. As the first point of contact, enrollment agents play a crucial role in determining whether patients can benefit from care coordination and management programs.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pdig.0000992.t005(source: https://plos.figshare.com/articles/dataset/Topic_interpretation_for_the_5-topic_model_/30160679)。
数据缺失?
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