Overview of the CLEF eHealth Evaluation Lab 2020

Goeuriot, Lorraine; Suominen, Hanna; Kelly, Liadh; Miranda-Escalada, Antonio; Krallinger, Martin; Liu, Zhengyang; Pasi, Gabriella; Saez, Gabriela Gonzalez; Viviani, Marco; Xu, Chenchen

doi:10.1007/978-3-030-58219-7_19

Cited by 21 publications

(7 citation statements)

References 26 publications

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“…While checking for relevance, users’ knowledge level is not considered, which could play a major role in the relevance of a resource to an individual’s information need. For example, if a person with no medical background is provided with academic literature to fulfill their information needs, it may be difficult for them to understand the literature [ 20 , 75 , 76 ]. Similarly, other aspects of relevance, including the trustworthiness and clinical validity of the document, were not considered in this study [ 23 , 77 ].…”

Section: Discussionmentioning

confidence: 99%

“…Despite the benefits of shared resources, some important questions arise, given that OHC users are health consumers and might not be health experts: which resources shared by the OHC peers are relevant to the information needs of survivors of OvCa and their caregivers, and what aspects of resource sharing can help us determine resource relevance? Previous research examined health literacy in OHCs and revealed that most of the content is generated by users with underdeveloped skills in validating information sources and navigating the internet [ 20 ]. Therefore, users need help in finding the relevant resources generated or shared in OHCs [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community

Thaker¹,

Chi²,

Birkhoff³

et al. 2022

JMIR Cancer

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Background Online health communities (OHCs) provide patients and survivors of ovarian cancer (OvCa) and their caregivers with help beyond traditional support channels, such as health care providers and clinicians. OvCa OHCs promote connections and exchanges of information among users with similar experiences. Users often exchange information, which leads to the sharing of resources in the form of web links. Although OHCs are important platforms for health management, concerns exist regarding the quality and relevance of shared resources. Previous studies have examined different aspects of resource-sharing behaviors, such as the purpose of sharing, the type of shared resources, and peer user reactions to shared resources in OHCs to evaluate resource exchange scenarios. However, there is a paucity of research examining whether resource-sharing behaviors can ultimately determine the relevance of shared resources. Objective This study aimed to examine the association between OHC resource-sharing behaviors and the relevance of shared resources. We analyzed three aspects of resource-sharing behaviors: types of shared resources, purposes of sharing resources, and OHC users’ reactions to shared resources. Methods Using a retrospective design, data were extracted from the National Ovarian Cancer Coalition discussion forum. The relevance of a resource was classified into three levels: relevant, partially relevant, and not relevant. Resource-sharing behaviors were identified through manual content analysis. A significance test was performed to determine the association between resource relevance and resource-sharing behaviors. Results Approximately 48.3% (85/176) of the shared resources were identified as relevant, 29.5% (52/176) as partially relevant, and 22.2% (39/176) as irrelevant. The study established a significant association between the types of shared resources (χ218=33.2; P<.001) and resource relevance (through chi-square tests of independence). Among the types of shared resources, health consumer materials such as health news (P<.001) and health organizations (P=.02) exhibited significantly more relevant resources. Patient educational materials (P<.001) and patient-generated resources (P=.01) were more significantly associated with partially relevant and irrelevant resources, respectively. Expert health materials, including academic literature, were only shared a few times but had significantly (P<.001) more relevant resources. A significant association (χ210=22.9; P<.001) was also established between the purpose of resource sharing and overall resource relevance. Resources shared with the purpose of providing additional readings (P=.01) and pointing to resources (P=.03) had significantly more relevant resources, whereas subjects for discussion and staying connected did not include any relevant shared resources. Conclusions The associations found between resource-sharing behaviors and the relevance of these resources can help in collecting relevant resources, along with the corresponding information needs from OvCa OHCs, on a large scale through automation. The results from this study can be leveraged to prioritize the resources required by survivors of OvCa and their caregivers, as well as to automate the search for relevant shared resources in OvCa OHCs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community

Thaker¹,

Chi²,

Birkhoff³

et al. 2022

JMIR Cancer

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“…The three most common information extraction tasks -named entity recognition (38,39,40), concept normalization (41,42), and relation extraction (Section 7) -are still active areas of research. However, in many cases, software systems exist that will perform these tasks automatically.…”

Section: Software For Clinical Information Extractionmentioning

confidence: 99%

Modern Clinical Text Mining: A Guide and Review

Percha

2021

Annu. Rev. Biomed. Data Sci.

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Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…This aim differs from our main objective: generating lay language summaries of biomedical scientific reviews for health consumers. Another related area of work concerns information retrieval from the internet, where the goal is to help consumers find (rather than interpret) health information (Goeuriot et al 2020).…”

Section: Biomedical Domain Summarizationmentioning

confidence: 99%

Automated Lay Language Summarization of Biomedical Scientific Reviews

Guo

Qiu

Wang

et al. 2021

AAAI

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Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current effort, providing insights and directions for future work.

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Overview of the CLEF eHealth Evaluation Lab 2020

Cited by 21 publications

References 26 publications

Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community

Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community

Modern Clinical Text Mining: A Guide and Review

Automated Lay Language Summarization of Biomedical Scientific Reviews

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