TY  - JOUR
AU  - Kamba, Masaru
AU  - Manabe, Masae
AU  - Wakamiya, Shoko
AU  - Yada, Shuntaro
AU  - Aramaki, Eiji
AU  - Odani, Satomi
AU  - Miyashiro, Isao
PY  - 2021
DA  - 2021/10/28
TI  - Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services:  Natural Language Processing-Based Approach
JO  - JMIR Cancer
SP  - e32005
VL  - 7
IS  - 4
KW  - natural language processing
KW  - internet use
KW  - patient generated health data
KW  - neoplasms
AB  - Background: A large number of patient narratives are available on various web services. As for web question and answer services, patient questions often relate to medical needs, and we expect these questions to provide clues for a better understanding of patients’ medical needs. Objective: This study aimed to extract patients’ needs and classify them into thematic categories. Clarifying patient needs is the first step in solving social issues that patients with cancer encounter. Methods: For this study, we used patient question texts containing the key phrase “breast cancer,“ available at the Yahoo! Japan question and answer service, Yahoo! Chiebukuro, which contains over 60,000 questions on cancer. First, we converted the question text into a vector representation. Next, the relevance between patient needs and existing cancer needs categories was calculated based on cosine similarity. Results: The proportion of correct classifications in our proposed method was approximately 70%. Considering the results of classifying questions, we found the variation and the number of needs. Conclusions: We created 3 corpora to classify the problems of patients with cancer. The proposed method was able to classify the problems considering the question text. Moreover, as an application example, the question text that included the side effect signaling of drugs and the unmet needs of cancer patients could be extracted. Revealing these needs is important to fulfill the medical needs of patients with cancer. 
SN  - 2369-1999
UR  - https://cancer.jmir.org/2021/4/e32005
UR  - https://doi.org/10.2196/32005
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34709187
DO  - 10.2196/32005
ID  - info:doi/10.2196/32005
ER  -