Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis.

BACKGROUND: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. OBJECTIVE: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. METHODS: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. RESULTS: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. CONCLUSIONS: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.

Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning.

In this study, we implemented machine learning models that can detect suicidality posts on Twitter. We randomly selected and annotated 20,000 tweets and explored metadata and text features to build effective models. Metadata features were studied in great details to understand their possibility and importance in suicidality detection models. Results showed that posting type (i.e., reply or not) and time-related features such as the month, day of the week, and the time (AM vs. PM) were the most important metadata features in suicidality detection models. Specifically, the probability of a social media post being suicidal is higher if the post is a reply to other users rather than an original tweet. Moreover, tweets created in the afternoon, on Fridays and weekends, and in fall have higher probabilities of being detected as suicidality tweets compared with those created in other times. By integrating metadata and text features, we obtained a model of good performance (i.e., F1 score of 0.846) that can assist humans in the real-world setting to detect suicidality social media posts.

Understanding information behavior of South Korean Twitter users who express suicidality on Twitter.

Objective: Although there were few studies on how suicidal users behave on Twitter, they only investigated partial aspects such as tweeting frequency and tweet length. Therefore, we aim to understand the various information behavior of suicidal users in South Korea. Methods: To achieve this goal, we annotated 20,000 tweets and identified 1097 tweets with the expression of suicidality (i.e. suicidal tweets) and 229 suicidal users (i.e. experimental group). Using the data, a user profile analysis, comparative analysis with control group, and tweets/hashtags analysis were performed. Results: Our results show that many suicidal users used suicide-related keywords in their user IDs, usernames, descriptions, and pinned tweets. We also found that, compared to the control group, the experimental group show different patterns of information behavior. The experimental group did not frequently use Twitter and, on average, wrote longer texts than the control group. A clear seasonal pattern was also identified in the experimental group's tweeting behavior. Frequently used keywords/hashtags were extracted from tweets written by the experimental group for the purpose of understanding their concerns and detecting more suicidal tweets. Conclusions: We believe that our study will help in the understanding of suicidal users' information behavior on social media and lay the basis for more accurate actions for suicide prevention and early intervention on social media.

Linking suicide and social determinants of health in South Korea: An investigation of structural determinants.

Introduction: Studies have shown that suicide is closely related to various social factors. However, due to the restriction in the data scale, our understanding of these social factors is still limited. We propose a conceptual framework for understanding social determinants of suicide at the national level and investigate the relationships between structural determinants (i.e., gender, employment statuses, and occupation) and suicide outcomes (i.e., types of suicide, places of suicide, suicide methods, and warning signs) in South Korea. Methods: We linked a national-level suicide registry from the Korea Psychological Autopsy Center with the Social Determinants of Health framework proposed by the World Health Organization's Commission on Social Determinants of Health. Results: First, male and female suicide victims have clear differences in their typical suicide methods (fire vs. drug overdose), primary warning signs (verbal vs. mood), and places of death (suburb vs. home). Second, employees accounted for the largest proportion of murder-suicides (>30%). The proportion of students was much higher for joint suicides than for individual suicides and murder-suicides. Third, among individuals choosing pesticides as their suicide method, over 50% were primary workers. In terms of drug overdoses, professionals and laborers accounted for the largest percentage; the former also constituted the largest proportion in the method of jumping from heights. Conclusion: A clear connection exists between the investigated structural factors and various suicide outcomes, with gender, social class, and occupation all impacting suicide.

Mapping scientific landscapes in UMLS research: a scientometric review.

OBJECTIVE: The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. MATERIALS AND METHODS: We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. RESULTS: The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain's growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. DISCUSSION: Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. CONCLUSION: The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.