Patient Concerns and Perceptions Regarding Biologic Therapies in Ankylosing Spondylitis: Insights From a Large-Scale Survey of Social Media Platforms.

OBJECTIVE: Few studies have examined ankylosing spondylitis (AS) patients' concerns about and perceptions of biologic therapies, apart from traditional surveys. In this study, we used social media data to examine the knowledge, attitudes, and beliefs of AS patients regarding biologic therapies. METHODS: We collected posts published on 601 social media sites between January 1, 2016 and April 26, 2017. In each post, both an AS keyword and a biologic were mentioned. To explore themes within the collection of posts in an unsupervised manner, a latent Dirichlet allocation topic model was fit to the data set. Each discovered topic was represented as a discrete distribution over the words in the collection, similar to a word cloud. The topics were manually reviewed to identify themes, which were confirmed using thematic data analysis. RESULTS: We examined 27,416 social media posts and identified 112 themes. The majority of themes (n = 67 [60%]) focused on discussions related to AS treatment. Other themes, including the psychological impact of AS, reporting of medical literature, and AS disease consequences, accounted for the remaining 40% (n = 45). In discussions regarding AS treatment, most topics involved biologics, and most subthemes involved side effects (e.g., fatigue, allergic reactions), biologic treatment attributes (e.g., dosing, frequency), and concerns about use of biologics (e.g., increased cancer risk). Additional implicit patient needs (e.g., support) were identified using qualitative analyses. CONCLUSION: Social media revealed a dynamic range of themes governing AS patients' experience with and choice of biologic agents. The complexity of selecting biologics from among many such agents and navigating their risk/benefit profiles suggests the merit of creating online tools tailored to support patients' decision-making with regard to biologic therapies for AS.

Developing the Transdisciplinary Aging Research Agenda: New Developments in Big Data.

BACKGROUND: Advances in big data analytics can enable more effective and efficient research processes, with important implications for aging research. Translating these new potentialities to research outcomes, however, remains a challenge, as exponentially increasing big data availability is yet to translate into a commensurate era of 'big knowledge,' or exponential increases in biomedical breakthroughs. Some argue that big data analytics heralds a new era associated with the 'end of theory.' According to this perspective, correlation supersedes causation, and science will ultimately advance without theory and hypotheses testing. On the other hand, others argue that theory cannot be subordinate to data, no matter how comprehensive data coverage may ultimately become. OBJECTIVE: Given these two tensions, namely (i) between exponential increases in data that have not translated into exponential increases in biomedical research outputs; and (ii) between the promise of comprehensive data coverage and inductive data-driven modes of enquiry versus theory-driven deductive modes, this critical review seeks to offer useful perspectives of big data analytics and to derive certain theoretical implications for aging research. METHOD: This work offers a critical review of theory and literature relating big data to aging research. RESULT: The rise of big data provides important insights into the theory development process itself, highlighting potential for holistic theoretical assemblage to ultimately enable near real time research capability. CONCLUSION: Big data may represent a new paradigm of aging research that can dramatically increase the rate of scientific breakthroughs, but innovative theory development remains key to this potential.

Text mining of cancer-related information: review of current status and future directions.

PURPOSE: This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research. METHODS: A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar. RESULTS: A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports.

[Advance of studies on metabolic fingerprinting analytical techniques and data processing methods].

Metabolomics is an emerging discipline subsequent to genomics, transcriptomics and proteomics, aiming for systematically studying the regularity of changes in metabolite to revealing organism's nature of movement and metabolism. It is especially important in modern pharmacological studies. Metabolic fingerprinting analysis is a method for metabolic analysis on high throughput of all metabolites, studying changes in drugs, organisms and endogenic metabolites caused by drugs and finding out related biomarkers to reflect dynamic changes inside organisms more directly and explain the mechanism of drugs and their effects on diseases. This essay summarizes some new metabolic fingerprint analytical methods and data processing methods used for metabolic fingerprint, elaborates their advantages and disadvantages and looks ahead to their combination with studies on traditional Chinese medicines, providing room for the development of new methods and new approaches for studies on complexity theory system of traditional Chinese medicines.