John H. Holmes

Summary Objectives: We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods: We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results: A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions: Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains.Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

Context: The rapid emergence of new technologies support collection and use of a wide variety of data from clinical, genomic, social and behavioral, environmental, and financial sources, and have a great impact on the governance of personal health information.Papers in the special issue: The papers in this special issue on governance touch on the topic from a variety of focuses, including leadership perspectives, local and federal case studies, and the future importance of patient engagement.Themes: This special issue focuses on three major themes—that data governance is growing in importance and presenting new challenges that must be addressed, that health care organizations must prioritize governance design, implementation, and functions as a priority, and that governance seems to be naturally converging on an archetype as described by this set of papers.Future State of Governance: In order to deal with issues such as data de- and re-identification, data governance must be studied as its own field.

Objective To review the published, peer-reviewed literature on clinical research data warehouse governance in distributed research networks (DRNs).Ma

Objective: To examine public and media response to the draft ( O ctober 2011) and finalised ( M ay 2012) recommendations of the U nited S tates Preventive Services Task Force ( USPSTF ) against prostate‐specific antigen ( PSA ) testing via Twitter, a popular social network with over 200 million active users. Materials and Methods: We used a mixed‐methods design to analyse posts on Twitter, known as ‘tweets’. Using the search term ‘prostate cancer’, we archived tweets in the 24‐h periods following the release of both the draft and the finalised USPSTF recommendations. We recorded tweet rate per h and developed a coding system to assess the type of user and sentiment expressed in tweets and linked articles. Results: After the draft and finalised USPSTF recommendations were released, 2042 and 5357 tweets focused on the USPSTF report, respectively. The tweet rate nearly doubled within 2 h of both announcements. Fewer than 10% of tweets expressed an opinion about screening, and the majority of these were pro‐screening during both periods. By contrast, anti‐screening articles were tweeted more frequently in both the draft and finalised study periods. Between the draft and the finalised recommendations, the proportion of anti‐screening tweets and anti‐screening article links increased ( P = 0.03 and P < 0.01, respectively). Conclusions: There was increased Twitter activity surrounding the USPSTF draft and finalised recommendations. The percentage of anti‐screening tweets and articles appeared to increase, perhaps due to the interval public comment period. Despite this, most tweets did not express an opinion, suggesting a missed opportunity in this important arena for advocacy.

There are millions of public posts to medical message boards by users seeking support and information on a wide range of medical conditions. It has been shown that these posts can be used to gain a greater understanding of patients’ experiences and concerns. As investigators continue to explore large corpora of medical discussion board data for research purposes, protecting the privacy of the members of these online communities becomes an important challenge that needs to be met. Extant entity recognition methods used for more structured text are not sufficient because message posts present additional challenges: the posts contain many typographical errors, larger variety of possible names, terms and abbreviations specific to Internet posts or a particular message board, and mentions of the authors’ personal lives. The main contribution of this paper is a system to de-identify the authors of message board posts automatically, taking into account the aforementioned challenges. We demonstrate our system on two different message board corpora, one on breast cancer and another on arthritis. We show that our approach significantly outperforms other publicly available named entity recognition and de-identification systems, which have been tuned for more structured text like operative reports, pathology reports, discharge summaries, or newswire.