Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Crowdsourcing is “the practice of obtaining participants, services, ideas, or content by soliciting contributions from a large group of people, especially via the Internet.” (Ranard et al. J. Gen. Intern. Med. 29:187, 2014) Although crowdsourcing has been adopted in healthcare research and its potential for analyzing large datasets and obtaining rapid feedback has recently been recognized, no systematic reviews of crowdsourcing in cancer research have been conducted. Therefore, we sought to identify applications of and explore potential uses for crowdsourcing in cancer research. We conducted a systematic review of articles published between January 2005 and June 2016 on crowdsourcing in cancer research, using PubMed, CINAHL, Scopus, PsychINFO, and Embase. Data from the 12 identified articles were summarized but not combined statistically. The studies addressed a range of cancers (e.g., breast, skin, gynecologic, colorectal, prostate). Eleven studies collected data on the Internet using web‐based platforms; one recruited participants in a shopping mall using paper‐and‐pen data collection. Four studies used Amazon Mechanical Turk for recruiting and/or data collection. Study objectives comprised categorizing biopsy images (n = 6), assessing cancer knowledge (n = 3), refining a decision support system (n = 1), standardizing survivorship care‐planning (n = 1), and designing a clinical trial (n = 1). Although one study demonstrated that “the wisdom of the crowd” (NCI Budget Fact Book, 2017) could not replace trained experts, five studies suggest that distributed human intelligence could approximate or support the work of trained experts. Despite limitations, crowdsourcing has the potential to improve the quality and speed of research while reducing costs. Longitudinal studies should confirm and refine these findings.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

Objective: The study sought to explore online health communities (OHCs) for sexual minority women (SMW) with cancer by conducting computational text analysis on posts. Materials and Methods: Eight moderated OHCs were hosted by the National LGBT Cancer Network from 2013 to 2015. Forty-six SMW wrote a total of 885 posts across the OHCs, which were analyzed using Linguistic Inquiry and Word Count and latent Dirichlet allocation. Pearson correlation was calculated between Linguistic Inquiry and Word Count word categories and participant engagement in the OHCs. Latent Dirichlet allocation was used to derive main topics. Results: Participants (average age 46 years; 89% white/non-Hispanic) who used more sadness, female-reference, drives, and religion-related words were more likely to post in the OHCs. Ten topics emerged: coping, holidays and vacation, cancer diagnosis and treatment, structure of day-to-day life, self-care, loved ones, physical recovery, support systems, body image, and symptom management. Coping was the most common topic; symptom management was the least common topic. Discussion: Highly engaged SMW in the OHCs connected to others via their shared female gender identity. Topics discussed in these OHCs were similar to OHCs for heterosexual women, and sexual identity was not a dominant topic. The presence of OHC moderators may have driven participation. Formal comparison between sexual minority and heterosexual women’s OHCs are needed. Conclusions: Our findings contribute to a better understanding of the experiences of SMW cancer survivors and can inform the development of tailored OHC-based interventions for SMW who are survivors of cancer.