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This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed.

The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted.

It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums.

To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

The purpose of this study is to assess the opportunity for a distributed, networked open biomedical repository (OBR) using a knowledge management system (KMS) conceptual framework. An innovative KMS conceptual framework is proposed to guide the transition from a traditional, siloed approach to a sustainable OBR.

This paper reports on a cycle of action research, involving literature review, interviews and focus group with leaders in biomedical research, open science and librarianship, and an audit of elements needed for an Australasian OBR; these, along with an Australian KM standard, informed the resultant KMS framework.

The proposed KMS framework aligns the requirements for an OBR with the people, process, technology and content elements of the KM standard. It identifies and defines nine processes underpinning biomedical knowledge – discovery, creation, representation, classification, storage, retrieval, dissemination, transfer and translation. The results comprise an explanation of these processes and examples of the people, process, technology and content dimensions of each process. While the repository is an integral cog within the collaborative, distributed open science network, its effectiveness depends on understanding the relationships and linkages between system elements and achieving an appropriate balance between them.

The current research has focused on biomedicine. This research builds on the worldwide effort to reduce barriers, in particular paywalls to health knowledge. The findings present an opportunity to rationalize and improve a KMS integral to biomedical knowledge.

Adoption of the KMS framework for a distributed, networked OBR will facilitate open science through reducing duplication of effort, removing barriers to the flow of knowledge and ensuring effective management of biomedical knowledge.

Achieving quality, permanency and discoverability of a region’s digital assets is possible through ongoing usage of the framework for researchers, industry and consumers.

The framework demonstrates the dependencies and interplay of elements and processes to frame an OBR KMS.

This study aimed to examine the mental health information entities and associations between the biomedical, psychological and social domains of bipolar disorder (BD) by analyzing social media data and scientific literature.

Reddit posts and full-text papers from PubMed Central (PMC) were collected. The text analysis was used to create a psychological dictionary. The text mining tools were applied to extract BD entities and their relationships in the datasets using a dictionary- and rule-based approach. Lastly, social network analysis and visualization were employed to view the associations.

Mental health information on the drug side effects entity was detected frequently in both datasets. In the affective category, the most frequent entities were “depressed” and “severe” in the social media and PMC data, respectively. The social and personal concerns entities that related to friends, family, self-attitude and economy were found repeatedly in the Reddit data. The relationships between the biomedical and psychological processes, “afraid” and “Lithium” and “schizophrenia” and “suicidal,” were identified often in the social media and PMC data, respectively.

Mental health information has been increasingly sought-after, and BD is a mental illness with complicated factors in the clinical picture. This paper has made an original contribution to comprehending the biological, psychological and social factors of BD. Importantly, these results have highlighted the benefit of mental health informatics that can be analyzed in the laboratory and social media domains.

This paper aims to confirm the importance of developing knowledge, skills and attitudes in the permanent use of digital scientific information, which complements the training of professionals in the biomedical and health areas, considering that this type of user communities require up-to-date and truthful information for future decision-making which will directly affect the health of patients.

The study started from the elaboration of a diagnosis on learning styles in a student community at the undergraduate level in the area of biomedical and health sciences of the Autonomous University of San Luis Potosí (Mexico) (UASLP), through the application of the Honey-Alonso Learning Styles questionnaire (CHAEA), and by using such results it was possible to derive learning strategies for the achievement of digital information competencies that were effective in practice.

According to the diagnostic results, the learning styles with the greatest presence in students in the area of Biomedical and Health Sciences were identified and from this, precise didactic strategies were derived to enhance information skills in the use of digital sources. For this case, the Big6 Model was used and its implementation was combined (face-to-face and virtual) in the academic community studied, integrating an information skills development program in the digital library of the Center for Information in Biomedical Sciences (CICBI) from the university itself.

A practical experience is presented, which also offered concrete solutions, based on particular characteristics of the users. There is a low presence of studies of digital libraries’ users that consider aspects related to learning styles, especially applied from the perspective of information sciences and the digital library.

Gene term variation is a shortcoming in text‐mining applications based on biomedical literature‐based knowledge discovery. The purpose of this paper is to propose a technique for normalizing gene names in biomedical literature.

Under this proposal, the normalized forms can be characterized as a unique gene symbol, defined as the official symbol or normalized name. The unification method involves five stages: collection of the gene term, using the resources provided by the Entrez Gene database; encoding of gene‐naming terms in a table or binary matrix; design of a parametrized finite‐state graph (P‐FSG); automatic generation of a dictionary; and matching based on dictionary look‐up to transform the gene mentions into the corresponding unified form.

The findings show that the approach yields a high percentage of recall. Precision is only moderately high, basically due to ambiguity problems between gene‐naming terms and words and abbreviations in general English.

The major limitation of this study is that biomedical abstracts were analyzed instead of full‐text documents. The number of under‐normalization and over‐normalization errors is reduced considerably by limiting the realm of application to biomedical abstracts in a well‐defined domain.

The system can be used for practical tasks in biomedical literature mining. Normalized gene terms can be used as input to literature‐based gene clustering algorithms, for identifying hidden gene‐to‐disease, gene‐to‐gene and gene‐to‐literature relationships.

Few systems for gene term variation handling have been developed to date. The technique described performs gene name normalization by dictionary look‐up.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

The Information Services and Systems Department at King's College London addresses information literacy in a variety of ways. This paper will review all these approaches and discuss future plans. Design/methodology/approach A descriptive paper describing a three part model of good practice for promoting health information literacy: through training delivered as part of the taught undergraduate and postgraduate curriculum; through the iGrad programme aimed at research students; and through work with the Personnel department, developing staff knowledge and information competencies via TrainIT, a suite of IT and information retrieval courses.

That the model described is robust but faces future challenges: for example, the challenge of sheer growth in student numbers and widening participation initiatives, the need to re‐model the curriculum to involve more online learning and to centre around clinical scenarios, the challenge of optimising the relationship between the National Health Service (NHS) and higher education (HE) sectors.

In particular, the models of assessment used and analysis of future challenges present potential for further research analysis.

This paper offers many practice‐based examples of how to enhance levels of health information literacy.

The well developed methods of promoting information literacy outlined in this paper are worthy of note by practitioners both within and beyond the health information field.

Two trends, the decline in funding for information science research and the accelerating contention between the public and private sectors in the information field, pose a threat to government institutions that conduct and support information science research. The appropriateness of the activities of the National Library of Medicine (NLM) in particular has been questioned by the private sector. NLM, which has heretofore been a leader in information science research and development and in serving the health professional community (as mandated by law) is threatened. Private sector opponents should bear in mind that the contributions made by the Government both directly through contracts and indirectly through education and spinoff from research efforts have paved the way and laid the groundwork for the online information industry. Continued growth of the industry will require continued research.

A review was carried out of the ‘information landscape’ within the pharmaceuticals‐based molecular biology community, which examined the research problems requiring biological‐sequence data, important sources of information, methods of access, information‐seeking behaviour of end users and the role of libraries and information centres. This work concentrated on the practical aspects of how biological sequence information is managed and used in a research setting and was carried out as part of the MSc in Information Science at the City University. Fifteen questionnaires were sent to information scientists in the UK pharmaceutical industry and a user study was carried out amongst scientists at Celltech. Most of the important primary data are available freely or cheaply via the Internet and molecular biologists were found to be self‐reliant in their use of these resources. Currency of information was found to be very important in the research process and the issue of Internet security was taken very seriously. Most questionnaire respondents saw a productive role in the future for information workers in the field of molecular biology, citing end‐user training and data integration as possible roles, although the degree of involvement will depend on the particular mix of skills and experience that exist within an information department.

The progress of initiatives concerned with implementing evaluated clinical research (such as evidence based medicine and clinical effectiveness) is dependent on the way individual health professionals actually acquire, use and value clinical knowledge in routine practice. The findings of two research projects, the Value and EVINCE projects, are compared with studies of the consolidation and application of clinical knowledge in clinical decision making. The Value project was concerned with the ways in which information from NHS libraries might be used in present and future clinical decision making. EVINCE was a similar impact study for nursing professionals. Both studies confirmed the importance of personal clinical knowledge. Health information services need to use a variety of strategies and knowledge management skills to ensure that the evaluated research evidence is assimilated and implemented into practice.