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.

BACKGROUND Currently, a large number of patient narratives are available on various web services. On web question and answer (QA) services, patient questions often relate to medical needs. Therefore, we expect these questions to provide clues to understanding patients’ medical needs. OBJECTIVE This study aims to extract patient needs and classify them into thematic categories. To clarify the patient's needs would be the first step to solve social issues for cancer patients. METHODS The material of this study is patient question texts containing the keyword “breast cancer" in the Yahoo! Japan QA service, Yahoo! Chiebukuro, which contains over 60,000 questions on cancer. First, we convert the question text into a vector representation; then, the relevance between patient needs and existing cancer needs categories are calculated based on cosine similarity. RESULTS The proportion of correct classifications in our proposed method is approximately 70%. We reveal the variation and the number of needs from the results of classifying questions. CONCLUSIONS There are various clinical applications to applying the proposed method such as identifying the side effect signaling of drugs and the unmet needs of cancer patients. Revealing these needs is important to satisfy the medical needs of cancer patients.

BACKGROUND Even though COVID-19 is a pandemic, its impact is not only limited to public health but has spanned across economy, education, work style or social relationships. As the COVID-19 studies proliferating in the past two years, whether the studies would yield insights that are relevant to the individuals in the society becomes important. OBJECTIVE This study focuses on uncovering and tracking the concerns in Japan across the COVID-19 period by investigating Japanese individuals' self-disclosing of life plan disruption on the social media, hence, yielding field evidence of which concerns might warrant further addressing for individuals living in Japan. METHODS We have extracted 300,778 tweets using "Corona no-sei (due to COVID-19, because of COVID-19, or considering COVID-19) as a query phrase, which allowed us to identify the activities and life plans disrupted due to COVID-19. The number of tweets compares with the number of COVID-19 cases to analyze the correlation. In addition, we analyze frequent co-occurrence words. RESULTS Our findings showed that while education surfaced as the top concern when Japanese government announced the first state of emergency. We also observed a sudden surge of anxiety about shortage of materials such as toilet paper. While these concerns were relatively short-term concerns, as the pandemic dragged further and more state of emergencies being announced, more concerns about long-term life plans such as career, social relationships, and education started showing. CONCLUSIONS Overall, by adding the analysis on "Corona no-sei" to the conventional symptom-based monitoring, we were able to identify the underlying concerns at the peak of the disruption and across the whole time span of the three announcements of state of emergency.