Leveraging medical taxonomies to improve knowledge management within online communities of practice: The knowledge maps system

An empirical study of knowledge mapping implementation in Indonesian organizational context

Purpose

Knowledge growth is currently experiencing a very exponential increase, through a digital transformation of knowledge in the organization and the emergence of various platforms through management system content such as blogs and social media that have encouraged such growth. An employee no longer only interacts in knowledge management (KM) in his organization but can communicate and collaborate to share knowledge through these platforms widely. Much of the spread of knowledge is a challenge for organizations to manage and use their knowledge and spread knowledge and become part of the expertise possessed by employees in their organizations. For this reason, an intelligent knowledge mapping approach is needed to facilitate finding, managing and identifying knowledge owned by an organization. This paper aims to see the use of smart knowledge mapping in organizations empirically.

Design/methodology/approach

This research was conducted empirically by conducting a literature study method to compile a questionnaire and conduct a survey through purposive sampling, sampling taken from the KM and Society Indonesia community. This survey limits only to respondents who have experience in conducting knowledge mapping in an organization.

Findings

The use of data sources that have been owned by the organization and also the consideration of the use of digital footprint employees spread across the internet can be a potential to explore the knowledge resources possessed by the organization. Of course, with the support of technology through the use of artificial intelligence (AI), it is expected to be able to present a comprehensive, adaptive and objective knowledge mapping for the organization.

Practical implications

The paper provides practical implications for knowledge mapping implementation in an organization by adopting AI with considerable digital footprint employees spread across the internet.

Originality/value

This paper provides empirical information about knowledge mapping implementation in Indonesian organization.

Building Smart Knowledge Mapping Conceptual Model

Transformation in patterns of human interaction has shifted significantly in the use of the Internet, as these trends are rapidly evolving with the advent of online means for social interaction, such as social media, social networking, messaging, and so on. The transformation happens to give us challenges and opportunities to improve the quality of knowledge management undertaken by an organization. The most critical problem that in knowledge management issue is how to identify the state, ownership, and connectedness of critical knowledge in an organization visually. The studies to overcome these problems already done with a knowledge map approach, but in practice to do knowledge mapping is still done partially, either by the manual or automatic approach. Accordingly, this study aims to identify the critical components in building integrated knowledge maps from various electronic information sources that developed in the current Internet era. Through a literary study in an article that examines the map model of knowledge and by analysing smart terminology in several contexts from several kinds of literature. This study proposes a model of smart knowledge mapping conceptual model to be an initial model in developing a knowledge map that integrates various critical components.

A systematic literature review of machine learning in online personal health data

OBJECTIVE

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

MATERIALS AND METHODS

We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.

RESULTS

We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support.

CONCLUSIONS

The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.