Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT

Automated data analysis of unstructured grey literature in health research: A mapping review

\ua9 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. The amount of grey literature and ‘softer’ intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research

Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches

Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system

Identity Change through Affordances Actualization: Evidence from Healthcare Workers

As more and more digital technologies are used in healthcare organizations, the way healthcare workers work and doctor-patient communication are changing. These changes will lead to identity change of healthcare workers. Some scholars try to understand technological changes in terms of the affordance theory. However, there are few relevant studies that incorporate specific application scenarios. In this paper, we explore the specific performance of the digital technology affordance and the impact on healthcare workers’ identity in China. We conducted in-depth interviews with 14 healthcare workers and used grounded theory to summarize three kinds of digital technology affordance, namely functional affordance, process affordance and performance affordance. The findings suggest that on the one hand, digital technology affordance increase the efficiency of healthcare workers and enhance collaboration among colleagues, thus reinforcing the healthcare workers’ identity. On the other hand, over-reliance on digital technology may also lead to unnecessary hassles that worsen healthcare workers’ identity. Our study enriches the affordance theory and identity theory, and has constructive implications for the quality of healthcare services in a digital context

An Automated Method to Enrich and Expand Consumer Health Vocabularies Using GloVe Word Embeddings

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this dissertation, we present an automatic method to enrich existing concepts in a medical ontology with additional laymen terms and also to expand the number of concepts in the ontology that do not have associated laymen terms. Our work has the benefit of being applicable to vocabularies in any domain. Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. We improve these vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. By performing iterative feedback using GloVe’s candidate terms, we can boost the number of word occurrences in the co-occurrence matrix allowing our approach to work with a smaller training corpus. Our novel algorithms and GloVe were evaluated using two laymen datasets from the National Library of Medicine (NLM), the Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV) and the MedlinePlus Healthcare Vocabulary. For our first goal, enriching concepts, the results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Our best algorithm enhanced the corpus with synonyms from WordNet, outperformed GloVe with an F-score relative improvement of 25%. For our second goal, expanding the number of concepts with related laymen’s terms, our synonym-enhanced GloVe outperformed GloVe with a relative F-score relative improvement of 63%. The results of the system were in general promising and can be applied not only to enrich and expand laymen vocabularies for medicine but any ontology for a domain, given an appropriate corpus for the domain. Our approach is applicable to narrow domains that may not have the huge training corpora typically used with word embedding approaches. In essence, by incorporating an external source of linguistic information, WordNet, and expanding the training corpus, we are getting more out of our training corpus. Our system can help building an application for patients where they can read their physician\u27s letters more understandably and clearly. Moreover, the output of this system can be used to improve the results of healthcare search engines, entity recognition systems, and many others

The influence of affect on attitude

Priests of the medieval Catholic Church understood something about the relationship between affect and attitude. To instill the proper attitude in parishioners, priests dramatized the power of liturgy to save them from Hell in a service in which the experience of darkness and fear gave way to light and familiar liturgy. These ceremonies “were written and performed so as to first arouse and then allay anxieties and fears ” (Scott, 2003, p. 227): The service usually began in the dark of night with the gothic cathedral’s nave filled with worship-pers cast into total darkness. Terrifying noises, wailing, shrieks, screams, and clanging of metal mimicked the chaos of hell, giving frightened witnesses a taste of what they could expect if they were tempted to stray. After a prolonged period of this imitation of hell, the cathedral’s interior gradually became filled with the blaze of a thousand lights. As the gloom diminished, cacophony was supplanted by the measured tones of Gregorian chants and polyphony. Light and divine order replaced darkness and chaos (R. Scott, personal correspondence, March 15, 2004). This ceremony was designed to buttress beliefs by experience and to transfigure abstractions into attitudes. In place of merely hearing about “the chaos and perdition of hell that regular performances of liturgy were designed to hold in check ” (Scott, 2003), parishioners shoul

Predicting pediatric off-label drug use in Chinese hospitals: an application of the theory of planned behavior

The use of drugs is authorized according to guarantees given by drug makers concerning the clinical effectiveness of the drug under the condition that the drug administration follows specific guidelines. These guidelines are indicated in the drug package inserts reflecting knowledge from clinical trials. However, pediatric drugs are rarely included in the study of clinical trials as a result of special physiological and psychological characteristics of users. This leads to a lack of data relating to safety and effectiveness of pediatric marketed drug user groups. Thus, doctors often opt for off-label use of drugs, meaning, they prescribe, use administration route or dose beyond package insert issued by authority. Such off-label use is considered inevitable due to treatment of children diseases. Many countries have made explicit laws, regulations or rules on off-label drug use. In China, off-label drug use has become more common, but not conforming to regulations. In addition, there was no clear stipulation on off-label drug use, especially pediatric off-label drug use. As a result, doctors' medical orders, drug dispensing and distribution of pharmacists, and drug user groups were at greatest risks, laying hidden dangers for doctor-patient disputes. This implies, it is important to generate a behavioral model that allows for the prediction of off-label drug use in pediatrics. Objective: From the perspective of the Theory of Planned Behavior, this research aims to generate a predictive model of off-label drug use in Chinese hospitals. This model adds to extant state-of-art on behavioral models in off-label pediatric use, and it also intends to help normalize doctors' off-label drug use, prevent and treat drug abuse, and ensure the interests of patients and treatment demands. Methods: The present research included objective analysis on pediatric drug information of commonly used pediatric drugs package inserts and subjective analysis on behavior and cognition of pediatricians to model effects on off-label prescription. Results: Findings show pediatricians use of information concerning children's commonly-used drugs in China varied with different subpopulations and drugs. Doctors with different titles have issued off-label prescription in different frequencies. The explanatory model has predictive power on behavioral intention and off-label behavior. It was recommended that permission on issuance of off-label prescription should be limited. As for treatment of common diseases and rare, refractory diseases, different levels of off-label drug uses should be formulated for generic drugs and high-risk drugs. Conclusion: There were subjective and objective reasons of pediatric off-label drug use in China, relating to lack of drug information and prescription behavior of doctors. The theory of planned behavior can be used to predict the behavior of pediatricians in off-label drug use. Countermeasure and suggestion for normalization of off-label drug use was proposed to provide reference and basis for normalizing clinical pediatric off-label drug use. China's pediatric off-label drug use can and should be implemented in terms of academics, management, operation, and technologyA utilização de fármacos é autorizada mediante garantias dadas pelas farmacêuticas no que respeita à eficácia clínica sob a condição de que os fármacos sejam administrados de acordo com orientações específicas. Estas orientações estão indicadas nos folhetos informativos refletindo o conhecimento dos ensaios clínicos. Contudo, os fármacos pediátricos são raramente incluídos nos ensaios clínicos devido às características fisiológicas e psicológicas especiais dos utilizadores. Isto leva a uma falta de informação relativa à segurança e eficácia de fármacos comercializados para este grupo de utilizadores. Assim, os médicos frequentemente optam por uma utilização off-label, o que significa que prescrevem, administram ou doseiam para além do indicado nos folhetos informativos aprovados pelas autoridades. Tal uso off-label é considerado inevitável devido ao tratamento de doenças pediátricas. Na China, a utilização off-label de medicamentos tornou-se comum, mas não em conformidade com o regulamentado. Como resultado, as prescrições médicas, distribuição e dispensa pelos farmacêuticos face a grupos de utilizadores podem criar riscos traduzindo-se em disputas médico-paciente. Tal implica que é importante gerar um modelo comportamental que permita prever o uso off-label de medicamentos no contexto pediátrico. Objectivo: Utilizando a teoria do comportamento planeado, pretende a presente investigação gerar um modelo preditivo da utilização off-label de medicamentos em contexto hospitalar na China. Este modelo acresce ao estado da arte dos modelos comportamentais na utilização off-label pediátrica e também procura ajudar a normalizar o uso off-label de medicamentos por parte dos médicos, a prevenir e tratar o abuso medicamentoso, e a garantir o interesse dos pacientes e as exigências terapêuticas. Método: O estudo presente compreende a investigação objetiva de folhetos informativos de fármacos comummente usados em pediatria bem como investigação subjetiva sobre o comportamento e cognições dos pediatras relativos à prescrição off-label. Resultados: A utilização de informação relativa à administração pediátrica de fármacos comuns na China varia de acordo com as diferentes subpopulações pediátricas e o tipo de fármaco. Os médicos com variadas categorias profissionais têm prescrito off-label com frequência diversa. Foi recomendado que a permissão para prescrever off-label seja limitada. Para o tratamento de doenças comuns e raras, devem ser formulados diferentes níveis de prescrição off-label diferenciando os fármacos de baixo e alto risco. Conclusão: Há motivos subjetivos e objetivos para que ocorra uso off-label de medicamentos na China, que se relacionam com a falta de informação farmacológica e o comportamento de prescrição dos médicos. A teoria do comportamento planeado pode ser mobilizada para prever o comportamento dos pediatras relativo aos usos off-label. São propostas medidas corretivas e sugestões para a normalização do uso off-label para facultar uma referência e a base para normalizar o uso clínico pediátrico off-label de fármacos. A utilização off-label de fármacos na China pode e deve ser implementada com base no conhecimento científico, gestão, operação e tecnologia