Breast Cancer Symptom Clusters Derived From Social Media and Research Study Data Using Improved $K$ -Medoid Clustering-Reference-Cited by-同舟云学术

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Breast Cancer Symptom Clusters Derived From Social Media and Research Study Data Using Improved $K$ -Medoid Clustering

Published:2016-06 Issue:2 Volume:3 Page:63-74
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Ping Qing,Yang Christopher C.^ORCID,Marshall Sarah A.,Avis Nancy E.,Ip Edward H.^ORCID

Funder

National Science Foundation

National Institutes of Health

U.S. Department of Defense

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://ieeexplore.ieee.org/ielaam/6570650/7752996/7737067-aam.pdf

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators;Frontiers in Medicine;2024-03-06

2. Identification and validation of KIF23 as a hypoxia-regulated lactate metabolism-related oncogene in uterine corpus endometrial carcinoma;Life Sciences;2024-03

3. BI-RADS Score Prediction Using AI for Breast Cancer Screening;Lecture Notes in Networks and Systems;2024

4. Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms;Physica Medica;2023-10

5. Symptom clusters among breast cancer patients in relation to chemotherapy cycles: a longitudinal study;Supportive Care in Cancer;2023-09-12

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