Back to search

Article

Exploring Internet hospital patient demand patterns from online consultation content using text clustering

2025-10-30

Abstract excerpt

<title>Abstract</title> <p> <bold>Background</bold> Internet hospital patient generated questions during online consultations are typically short, semantically incomplete, and expressed in a non-standardized manner, which poses challenges for demand recognition and knowledge discovery. This study aims to identify core categories of patient concerns through text clustering based on real-world online consultation...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
6ae60cc3-ab67-5242-a03d-78cea87c1bd2
DOI
10.21203/rs.3.rs-7797643/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Exploring Internet hospital patient demand patterns from online consultation content using text clusteringDOI 10.21203/rs.3.rs-7797643/v1
Prioritising Hospital Complaints: An Innovative Tool Using Large Language Model-Assisted Content Analysis and Machine Learning AlgorithmsSemantic proximity
Unsupervised text clustering with large language modelsSemantic proximity
Demand attributes of Internet+ nursing services among general outpatients: a cross-sectional study based on the Kano modelSemantic proximity
In-depth expansion of Internet hospital services and multi-dimensional exploration of AI-empowered medical servicesSemantic proximity
Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning<strong> </strong>Semantic proximity
User feedback on the NHS Test & Trace Service during COVID-19: the use of machine learning to analyse free-text data from 37,914 UK adultsSemantic proximity
Generating actionable insights from free-text care experience survey data using qualitative and computational text analysis: A study protocolSemantic proximity
How to diagnose an overcrowded emergency department from its EHRs? Enhancing opportunities and challenges of real-world data from a whole-system perspectiveSemantic proximity
A Data-Driven Framework for Generating Population-Linked Case Vignettes from Nationwide Triage DataSemantic proximity
Impact of Telemedicine through Social Media: A Study of Topics in User Comments on TwitterSemantic proximity
Online Reviews based Ranking of Hotels integrating Topic Modelling and Picture Fuzzy approachSemantic proximity
Optimizing Department-Specific Healthcare Delivery Using Quality Function Deployment in Multi-Criteria Recommender SystemSemantic proximity
Select a neighboring publication to make it the new centre.