Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Apr 20, 2021)
Date Submitted: May 14, 2019
Open Peer Review Period: May 17, 2019 - Jul 12, 2019
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Exploring Patient Needs in Chinese Online Health Communities: a Text Mining Method
Background:
Online Health Community refers to a forum where patients, their family members, doctors, and caregivers communicate. Patients who participate in Online Health Communities can obtain benefits for disease treatments and health management. Therefore, identifying the categories of patient needs are significant for enriching theories of patient needs and community construction.
Objective:
(1) Construct a patient needs corpus to explore patient needs in the Internet environment and analyze the similarities and differences of patient needs among Online Health Communities of different types and concerning different diseases. (2) Proposed a method for automatically identifying patient needs in Internet environments.
Methods:
This study used a combination of manual annotation and computer-aided methods to explore patient needs from four Chinese Online Health Communities. First, we recruited seven medical experts of diabetes or depression to label 9936 posts according to a theoretical framework, building a patient needs corpus in Internet environments. Second, we analyze the similarities and differences of patient needs among Online Health Communities of different types and concerning different diseases. Last, based on the corpus constructed by manual annotation, this research used Natural Language Processing, Machine Learning, and Deep Learning to train a model for automatically identifying patient needs.
Results:
According to statistical results, the proportion of posts related to patient needs in Online Health Communities was approximately 91%, and posts concerned with Emotional Support (18%), Information (28%), and Socialization (44%) needs were the top three most prevalent categories. Patient needs vary in Online Health Communities of different types, while there are few differences in patient needs between Diabetes and Depression Online Health Communities. Based on the corpus of manual annotation, a model was trained to identify patient needs automatically. This model takes Linguistic Features (LF) and Category Keyword Features (CKF) as input and Random Forest as the classifier, of which the F1 value was higher than 0.80 on the test set.
Conclusions:
Patient needs in the Internet environment mainly include Emotional Support needs, Information needs, and Socialization needs. Differences in community type and disease type can lead to diverse patient needs in Online Health Communities. It is practical to use computer-aided methods to identify patient needs in Online Health Communities automatically.
Clinicaltrial:
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