Abstract
Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.
Highlights
Online Health Communities (OHCs) help patients exchange experiences through posts [1]and are useful for patients with chronic conditions to help manage their health [2].Posts in OHCs often contain massive textual descriptions, including information on drug use, patient conditions, and patient-centered events [3]
On the basis of this dataset, we use the proposed evaluation metrics to examine the performance of our method in processing online posts in OHCs and the advantages it provides for improving knowledge-based services in OHCs
We develop four key metrics, Explicit Knowledge Rate (EKR), Latent Knowledge Rate (LKR), Knowledge Correlation Rate (KCR), and Perplexity of KI-TM (PK), to evaluate the expert knowledge found in the posts so the knowledge can improve the services provided by information systems in OHCs
Summary
Online Health Communities (OHCs) help patients exchange experiences through posts [1]and are useful for patients with chronic conditions to help manage their health [2].Posts in OHCs often contain massive textual descriptions, including information on drug use, patient conditions, and patient-centered events [3]. Online Health Communities (OHCs) help patients exchange experiences through posts [1]. Are useful for patients with chronic conditions to help manage their health [2]. Posts in OHCs often contain massive textual descriptions, including information on drug use, patient conditions, and patient-centered events [3]. With massive effective medical and healthcare information, OHCs can be used to predict crucial health events that online patients with life-changing illnesses may experience [4]. A knowledge-based OHC can help healthcare providers, online health information entrepreneurs, and developers make intelligent choices for patients and their caregivers [5,6]. The maintenance of an effective OHC requires effective community management to provide additional online support to patients. Information systems in current OHCs continue to encounter difficulties in the automatic extraction of meaningful relationships from various types of medical information in posts because health-related narrative posts are highly complex [8,9,10,11]
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