Discovering medication patterns for high-complexity drug-using diseases through electronic medical records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework

Competing by Saving Lives: How Pharmaceutical and Medical Device Companies Create Shared Value in Global Health

This report looks at how pharmaceutical and medical device companies can create shared value in global health by addressing unmet health needs in low- and middle-income countries. Companies have already begun to reap business value and are securing competitive advantages in the markets of tomorrow

Immigrant Entrepreneurs in the Massachusetts Biotechnology Industry (2007)

Immigrant entrepreneurs are co-founders in 25.7 percent of Massachusetts Biotechnology firms. In 2006, these immigrant-founded biotechnology companies produced over $7.6 billion dollars in sales and employed over 4,000 workers. The foreign-born founders came from across the globe but in larger numbers from Europe, Canada or Asia. Their firms specialize in the most complex, risky, life science-intensive aspects of biotechnology to seek knowledge directly applicable to human health. Biotechnology is a crucial industry for Massachhusetts and the evidence strongly suggests that immigrants have been key contributors to this industry by establishing new businesses as well as bringing intellectual capital and thereby contributing significantly to the overall economic growth of the Commonwealth

Shared Value in Emerging Markets: How Multinational Corporations Are Redefining Business Strategies to Reach Poor or Vulnerable Populations

This report illuminates the enormous opportunities in emerging markets for companies to drive competitive advantage and sustainable impact at scale. It identifies how over 30 companies across multiple sectors and geographies design and measure business strategies that also improve the lives of underserved individuals

Machine learning for data integration in human gut microbiome

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine

Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach

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Co-word analysis and academic performance from the Australasian Journal of Educational Technology in Web of Science

This study has been funded by the I+D+i project: Active methodologies for learning through technological resources for the development of society; code: CNT 4315.Since its inception in 1985, the Australasian Journal of Educational Technology (AJET) has been dedicated to the diffusion of studies on the integration of technology in higher education. Its track record in this field has placed it in the first quartile of the Scimago Journal & Country Rank. The objective of the study was to reveal to the scientific community the journey and evolution that this journal has had throughout its existence in Web of Science. A bibliometric methodology was used, supported by a scientific mapping from a unit of analysis of 798 documents. For this reason, a co-word analysis can be a fundamental tool for understanding the characteristics of their production and their impact on the scientific community. There is an evident progressive evolution of the studies published in the Australasian Journal of Educational Technology, with a first phase focused on the design and implementation of educational technology in learning environments, a second phase focused on the enrichment of technology and its acceptance within the processes of teaching and learning, and finally a stage focused on student and teacher perceptions of the implementation of technology in the educational context.I+D+i project: Active methodologies for learning through technological resources for the development of society CNT 431