Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services:  Natural Language Processing-Based Approach

Kamba, Masaru; Manabe, Masae; Wakamiya, Shoko; Yada, Shuntaro; Aramaki, Eiji; Odani, Satomi; Miyashiro, Isao

doi:10.2196/32005

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…The second and third are “mammogram” and “pap test,” screening tests for breast cancer and cervical cancer, respectively. We chose cancer screening as an illustrative case as these are common cancer types that are often discussed on social media [ 3 , 55 ] such that research would benefit from identifying relevant content that does not explicitly mention these technical, formal screening tests.…”

Section: Resultsmentioning

confidence: 99%

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube

Tong¹,

Margolin²,

Chunara³

et al. 2022

JMIR Med Inform

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Background Common methods for extracting content in health communication research typically involve using a set of well-established queries, often names of medical procedures or diseases, that are often technical or rarely used in the public discussion of health topics. Although these methods produce high recall (ie, retrieve highly relevant content), they tend to overlook health messages that feature colloquial language and layperson vocabularies on social media. Given how such messages could contain misinformation or obscure content that circumvents official medical concepts, correctly identifying (and analyzing) them is crucial to the study of user-generated health content on social media platforms. Objective Health communication scholars would benefit from a retrieval process that goes beyond the use of standard terminologies as search queries. Motivated by this, this study aims to put forward a search term identification method to improve the retrieval of user-generated health content on social media. We focused on cancer screening tests as a subject and YouTube as a platform case study. Methods We retrieved YouTube videos using cancer screening procedures (colonoscopy, fecal occult blood test, mammogram, and pap test) as seed queries. We then trained word embedding models using text features from these videos to identify the nearest neighbor terms that are semantically similar to cancer screening tests in colloquial language. Retrieving more YouTube videos from the top neighbor terms, we coded a sample of 150 random videos from each term for relevance. We then used text mining to examine the new content retrieved from these videos and network analysis to inspect the relations between the newly retrieved videos and videos from the seed queries. Results The top terms with semantic similarities to cancer screening tests were identified via word embedding models. Text mining analysis showed that the 5 nearest neighbor terms retrieved content that was novel and contextually diverse, beyond the content retrieved from cancer screening concepts alone. Results from network analysis showed that the newly retrieved videos had at least one total degree of connection (sum of indegree and outdegree) with seed videos according to YouTube relatedness measures. Conclusions We demonstrated a retrieval technique to improve recall and minimize precision loss, which can be extended to various health topics on YouTube, a popular video-sharing social media platform. We discussed how health communication scholars can apply the technique to inspect the performance of the retrieval strategy before investing human coding resources and outlined suggestions on how such a technique can be extended to other health contexts.

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Section: Resultsmentioning

confidence: 99%

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube

Tong¹,

Margolin²,

Chunara³

et al. 2022

JMIR Med Inform

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“…Then the generated feature maps F2, F3, and F4 are upsampled to the size of F1 for concatenation. For extracting the global context information from various levels of feature maps, four dilated convolutions (AT conv 1, AT conv 2, AT conv 3, AT conv 4) [20] with various atrous rates (2,4,8,16) are employed in a parallel fashion. Finally, a convolution operation is applied to obtain the final feature map.…”

Section: B Scr Modulementioning

confidence: 99%

“…According to [3], the average misdetection rate among medical professionals is around 30%. In a recent survey [4], it was found that misdetection of breast cancer from mammograms is the main cause of legal suits against medical professionals. To overcome the above mention misdetection, computer-aided detection (CAD) systems have been developed by researchers to reduce false detection.…”

Section: Introductionmentioning

confidence: 99%

EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation

Chowdary¹,

Yoagarajah²

2024

IEEE J. Biomed. Health Inform.

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Segmentation of breast masses in digital mammograms is very challenging due to its complexity. The recent U-shaped encoder-decoder networks achieved remarkable performance in medical image segmentation. However, these networks have some limitations: a) The multi-scale context information is required to accurately segment mass but is not effectively extracted and utilized. b) The global context information is often ignored by the skip connection. To overcome these limitations and achieve better segmentation, we propose an Enhanced U-shaped Network (EU-Net). The proposed EU-Net comprises of 3 novel components: 1) dense block, which is employed in the encoder and the decoder in place of convolutional layers to achieve the multi-scale features. 2) Multi-Scale Feature Extraction and Fusion, which is used in the junction between the encoder and the decoder for further extracting and fusing the multi-scale context information. 3) Skip Connection Reconstruction, which is inserted between the encoder and the decoder at each stage, to redesign the skip connection and emphasize the global context information. Extensive experimental results under different settings show that the proposed EU-Net achieves superior performances than the previous state-of-the-art segmentation models, and other existing approaches on IN-Breast and CBIS-DDSM mammogram datasets. The generalization ability of the proposed EU-Net is evidenced through crossdataset and ternary dataset evaluation performance. In the ternary dataset evaluation, the model is trained and evaluated on the UDIAT breast ultrasound dataset without finetuning. The EU-Net achieves higher generalization performance in both evaluation experiments. These experiments collectively indicate the efficiency and high generalization ability of the proposed EU-Net.

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“…Additionally, for diabetes patients, social support categories identified in users' posts showed that they are most interested in achievement, congratulations, network support, seeking emotional support, seeking informational support and so on [18]. For critical diseases, a text mining study showed that worrying about cancer with subjective symptoms occurs most frequently among all breast cancer patient posts [19]. A qualitative study summarized information needs of individuals who have sexually transmitted infections in a web-based forums of the American Sexual Health Association, which found that psychosocial information need should be caught more attention [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Through manual labeling of the replies, the study found that most community users received effective responses to their information needs, which mainly took the form of answers, discussions, inquiries, and emotional support. Previous research has summarized the types of feedback as answers, inquiries, and emotional support [19]. This study identified a new form of feedback, namely, discussion, which not only provided more comprehensive supplementary information to the inquirer but also promoted communication among community users, thus increasing community activity.…”

Section: Plos Onementioning

confidence: 99%

Health information needs and feedback of users in the online TCM community

Nie,

Xu,

Wang

2024

PLoS ONE

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To improve the information service quality of the online Traditional Chinese Medicine (TCM) community, this study investigated users’ information needs, feedback and the relationship between them. Using qualitative content analysis, the basic characteristics of users’ needs were obtained. Logistic regression was used to explore the impact of different need characteristics of feedback. The main findings are as follows: 1) Disease consultation, health preservation, professional discussion, knowledge sharing and experience description are the major 5 types of information needs in the online TCM community; 2) Some users provided TCM-related information, such as the tongue image and the TCM four diagnosis; 3) A total of 78.8% of the posts received effective feedback, and the main types of feedback were answering, discussing, inquiring and emotional supporting; 4) Providing enough information can significantly and positively affect whether needs receive effective feedback, suggesting that users can present information about their condition in as many different formats as possible when articulating their needs.

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Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach

Cited by 8 publications

References 11 publications

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube

Search Term Identification Methods for Computational Health Communication: Word Embedding and Network Approach for Health Content on YouTube

EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation

Health information needs and feedback of users in the online TCM community

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