An Automatic Approach to Extending the Consumer Health Vocabulary

Monselise, Michal; Greenberg, Jane; Liang, Ou Stella; Pascua, Sonia M.; Kim, Heejun; Kelly, Mat; Boone, Joan; Yang, Christopher C.

doi:10.2478/jdis-2021-0003

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Many cancer patients and others are dependent on consumer terms to be mapped to the UMLS medical terms. The expanded CHV will help cancer patients and caregivers expand their health literacy of medical terminology and potentially improve health outcomes through improved decisionmaking [7]. According to Morselize and colleagues the last CHV update included other additional health social behaviors such as vaping which is linked to lung cancer.…”

Section: Challenges Of Consumer Health Vocabulary and Technology Toolsmentioning

confidence: 99%

Consumer Health Informatics for Cancer Treatment Engagement

Czart¹

2023

ACRCI

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Cancer is defined by the National Cancer Institute as a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body. Often cancers may start in any part of the body and begin with small tumors [1]. Engagement of a patient with a cancer diagnosis is determined by an individual's belief that they have a problem and what action they are willing to take. The field of Consumer Health Informatics (CHI) represents new possibilities for healthcare delivery as a framework for more personalized and holistic healthcare that fosters active selfmanagement of one's state of health in terms of both remediating health dysfunction and promoting wellness. Every patient goes through the initial issues of health belief and transtheoretical stages of changes in life. Engagement of a patient with a cancer diagnosis is determined by an individual's belief that they have a problem. Current work in these topics independently shows that cancer consumers/patients are open to technology for many aspects of everyday life. However, the challenges occur when addressing various types of cancers due to each situation being different. The needs for health information and the type of health information are different. Innovative technologies such as Augmented Reality (AR) which uses both mobile applications and paper target images. Cancer patients and caregivers may be willing to test innovative AR technology by downloading a mobile application and be provided with target images that may be printed out and view the material for educational purposes. Educational programs can be developed to continue the engagement started by the physician. By using AR and VR tools, engagement can extend beyond the exam room and into the world.

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Section: Challenges Of Consumer Health Vocabulary and Technology Toolsmentioning

confidence: 99%

Consumer Health Informatics for Cancer Treatment Engagement

Czart¹

2023

ACRCI

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“… Gu et al (2019) also tried to enriched the laymen vocabularies leveraging recent word embedding methods. A most recent work presented a method to enhance the consumer health vocabulary by associating laymen terms with relations from the MeSH ontology ( Monselise et al, 2021 ). Other recent work showed the variety of consumer vocabularies people use when writing their reviews ( Hovy, Melumad & Inman, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Ibrahim¹,

Gauch²,

Salman³

et al. 2021

PeerJ Computer Science

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Background Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Objective Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this paper, we present an automatic method to enrich laymen’s vocabularies that has the benefit of being able to be applied to vocabularies in any domain. Methods Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. Our approach further improves the consumer health vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. The basic GloVe and our novel algorithms incorporating WordNet were evaluated using two laymen datasets from the National Library of Medicine (NLM), Open-Access Consumer Health Vocabulary (OAC CHV) and MedlinePlus Healthcare Vocabulary. Results The results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Furthermore, our enhanced GloVe approach outperformed basic GloVe with an average F-score of 61%, a relative improvement of 25%. Furthermore, the enhanced GloVe showed a statistical significance over the two ground truth datasets with P < 0.001. Conclusions This paper presents an automatic approach to enrich consumer health vocabularies using the GloVe word embeddings and an auxiliary lexical source, WordNet. Our approach was evaluated used healthcare text downloaded from MedHelp.org, a healthcare social media platform using two standard laymen vocabularies, OAC CHV, and MedlinePlus. We used the WordNet ontology to expand the healthcare corpus by including synonyms, hyponyms, and hypernyms for each layman term occurrence in the corpus. Given a seed term selected from a concept in the ontology, we measured our algorithms’ ability to automatically extract synonyms for those terms that appeared in the ground truth concept. We found that enhanced GloVe outperformed GloVe with a relative improvement of 25% in the F-score.

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“…In addition to these tools, platforms such as the Helping Interdisciplinary Vocabulary Engineering (Monselise et al , 2021), the UMLS (Amos et al , 2020; Perez et al , 2018) and the National Center for Biomedical Ontology BioPortal have emerged to bolster interoperability. These platforms provide a more connected information ecosystem by integrating diverse vocabularies and offering data visualization via APIs or other interfaces (Bodenreider, 2004; Salvadores et al , 2013).…”

Section: Introductionmentioning

confidence: 99%

Enhancing health-care data integration via automated semantic mapping

Clunis

2023

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Purpose This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention. Design/methodology/approach A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard. Findings The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space. Originality/value The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

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An Automatic Approach to Extending the Consumer Health Vocabulary

Cited by 3 publications

References 17 publications

Consumer Health Informatics for Cancer Treatment Engagement

Consumer Health Informatics for Cancer Treatment Engagement

An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource

Enhancing health-care data integration via automated semantic mapping

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