A Park or A Highway

Nakikj, Drashko; Mamykina, Lena

doi:10.1145/2998181.2998339

Cited by 33 publications

(6 citation statements)

References 69 publications

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“…Combining the outcomes of attention processes yielded the multi-headed attention module, as determined using Equation (3).…”

Section: Roberta Pre-training Layer To Construct Word Vectorsmentioning

confidence: 99%

“…Relevant studies have indicated that roughly half of web-based health information users with chronic health issues may benefit from accessing online health information [ 2 ]. The Q&A structure of online health communities (OHCs) is becoming more and more popular, with diabetic patients seeking medical knowledge and diabetic self-management assistance [ 3 , 4 , 5 ].Online health communities store a significant amount of case information, medical knowledge, and prescription data, which serves as the hotspots for medical big data applications.…”

Section: Introductionmentioning

confidence: 99%

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Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Zhou

Liao

et al. 2023

Bioengineering

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The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients.

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“…Combining the outcomes of attention processes yielded the multi-headed attention module, as determined using Equation (3).…”

Section: Roberta Pre-training Layer To Construct Word Vectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Zhou

Liao

et al. 2023

Bioengineering

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“…Patients want their physicians to demonstrate professional expertise while delivering personal care and compassion ( Schattner et al, 2004 ). Many patients require emotional support ( Vlahovic et al, 2014 ), such as understanding, encouragement, empathy, affection, affirmation, validation, care, and concern ( Nakikj and Mamykina, 2017 ). In the context of OHCs, physicians express emotional support via homepage greeting messages.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

Why should I consult? The impact of social support on patient consultation in online healthcare communities

Liu

et al. 2022

Front. Psychol.

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The COVID-19 epidemic put the traditional healthcare system and offline consultation method under strain. Patient consultations through online healthcare communities (OHCs) provide patients and physicians with a more convenient and secure route. Based on the social support theory, this study explores the impact of three dimensions of social support from physicians—information diagnosticity, source credibility, and emotional support—on patient consultations in OHCs and their moderating effect on patients’ compliments. We utilized Python Spiders to retrieve data from Haodf.com and gathered 2,982 physician reports. The model uses OLS regression with fixed effect estimations. The results show that these three dimensions of social support are positively impacted by consultation. Furthermore, patients’ compliments weaken the positive relationship between the three dimensions of physicians’ social support and patient consultations. This study contributes to the literature on social support theory in OHCs by exploring the physicians’ social support dimension and its impact on patient consultation. Moreover, this study makes practical contributions to physicians and platform administrators in OHCs.

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“…The use of digital technology amongst people living with a range of long-term health conditions (LTHCs) to support self-management [1][2][3] has increased dramatically. Online forums and social networking sites in which people living with LTHCs can share their experiences and expertise with their peers-other people living with the same condition-have been extensively investigated in terms of the informational, emotional and social support they can offer [3][4][5][6][7][8]. More recently, digital health technologies to share and exchange health data have been investigated, either through electronic patient health records (ePHRs) between patient and healthcare professionals [9][10][11], or through personally generated health data shared with others [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Sharing health data with others is not without its risks: sharing data is found to pose threats to the privacy and security of personal data [17,18] and plays a role in trust, adoption and continued use of digital health technologies [8][9][10][11]19,20]. We employ concepts common in trust, identity, privacy and security (TIPS) research.…”

Section: Introductionmentioning

confidence: 99%

People with long-term conditions sharing personal health data via digital health technologies: A scoping review to inform design

et al. 2023

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The use of digital technology amongst people living with a range of long-term health conditions to support self-management has increased dramatically. More recently, digital health technologies to share and exchange personal health data with others have been investigated. Sharing personal health data with others is not without its risks: sharing data creates threats to the privacy and security of personal data and plays a role in trust, adoption and continued use of digital health technology. Our work aims to inform the design of these digital health technologies by investigating the reported intentions of sharing health data with others, the associated user experiences when using these digital health technologies and the trust, identity, privacy and security (TIPS) considerations for designing digital health technologies that support the trusted sharing of personal health data to support the self-management of long-term health conditions. To address these aims, we conducted a scoping review, analysing over 12,000 papers in the area of digital health technologies. We conducted a reflexive thematic analysis of 17 papers that described digital health technologies that support sharing of personal health data, and extracted design implications that could enhance the future development of trusted, private and secure digital health technologies.

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A Park or A Highway

Cited by 33 publications

References 69 publications

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

Why should I consult? The impact of social support on patient consultation in online healthcare communities

People with long-term conditions sharing personal health data via digital health technologies: A scoping review to inform design

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