Detecting Adverse Drug Events Using a Deep neural network Model

Adverse drug events represent a key challenge in public health, especially with respect to drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of drug-related information utilizing different types of machine learning algorithms to predict potential interactions between drugs. In this work, our focus is on the use of genetic information about the drugs, in particular, the protein sequence and protein structure of drug protein targets to predict potential interactions between drugs. We collected information on drug-drug interactions (DDIs) from the DrugBank database and divided them into multiple datasets based on the type of information, such as, chemical structure, protein targets, side effects, pathways, protein-protein interactions, protein structure, information about indications. We proposed a similarity-based Neural Network framework called protein sequence-structure similarity network (S3N), and used this to predict the novel DDI’s. The drug-drug similarities are computed using different categories of drug information based on multiple similarity metrics. We compare the results with those from the state-of-the art methods on this problem. Our results show that proposed method is quite competitive, at times outperforming the state-of-the-art. Our performance evaluations on different datasets showed the predictive performance as follows: Precision 91\%-98\%, Recall 90\%-96\%, F1 Score 86\%-95\%, AUC 88\%-99\% Accuracy 86\%-95\%. To further investigate the reliability of the proposed method, we utilize 158 drugs related to cardiovascular disease to evaluate the performance of our model and find out the new interactions among the drugs. Our model showed 90\% accuracy of detecting the existing drug interactions and identified 60 new DDI’s for the cardiovascular drugs. Our evaluation demonstrates the effectiveness of S3N in predicting DDI’s

Integrative bioinformatics and graph-based methods for predicting adverse effects of developmental drugs

Adverse drug effects are complex phenomena that involve the interplay between drug molecules and their protein targets at various levels of biological organisation, from molecular to organismal. Many factors are known to contribute toward the safety profile of a drug, including the chemical properties of the drug molecule itself, the biological properties of drug targets and other proteins that are involved in pharmacodynamics and pharmacokinetics aspects of drug action, and the characteristics of the intended patient population. A multitude of scattered publicly available resources exist that cover these important aspects of drug activity. These include manually curated biological databases, high-throughput experimental results from gene expression and human genetics resources as well as drug labels and registered clinical trial records. This thesis proposes an integrated analysis of these disparate sources of information to help bridge the gap between the molecular and the clinical aspects of drug action. For example, to address the commonly held assumption that narrowly expressed proteins make safer drug targets, an integrative data-driven analysis was conducted to systematically investigate the relationship between the tissue expression profile of drug targets and the organs affected by clinically observed adverse drug reactions. Similarly, human genetics data were used extensively throughout the thesis to compare adverse symptoms induced by drug molecules with the phenotypes associated with the genes encoding their target proteins. One of the main outcomes of this thesis was the generation of a large knowledge graph, which incorporates diverse molecular and phenotypic data in a structured network format. To leverage the integrated information, two graph-based machine learning methods were developed to predict a wide range of adverse drug effects caused by approved and developmental therapies

Interrupções de atividades de enfermeiros e a segurança do paciente: revisão integrativa da literatura

OBJETIVOS: identificar características relacionadas a la interrupción que sufren los enfermeros en su práctica profesional, así como evaluar las implicaciones para la seguridad del paciente. MÉTODO: fue realizada una revisión de literatura de tipo integradora, con búsqueda en las bases de datos Pubmed/Medline, LILACS, SciELO y Biblioteca Cochrane, utilizando los descriptores interruptions y patient safety. La fecha inicial no fue limitada y la fecha final fue 31 de diciembre de 2013, se identificaron 29 artículos que atendieran a los criterios de inclusión. RESULTADOS: todos los artículos revisados describieron la interrupción como un factor perjudicial a la seguridad del paciente. El análisis de estos estudios reveló tres categorías relevantes: características de la interrupción, implicaciones de la interrupción para la seguridad del paciente e intervenciones para minimizar las interrupciones. CONCLUSIÓN: la interrupción favorece la ocurrencia de errores en la salud. Así, se notó la necesidad de realizar nuevas investigaciones para comprender ese fenómeno y los efectos del mismo en la práctica clínica.OBJECTIVES: to identify characteristics related to the interruption of nurses in professional practice, as well as to assess the implications of interruptions for patient safety. METHOD: integrative literature review. The following databases were searched: Pubmed/Medline, LILACS, SciELO and Cochrane Library, using the descriptors interruptions and patient safety. An initial date was not established, but the final date was December 31, 2013. A total of 29 papers met the inclusion criteria. RESULTS: all the papers included describe interruptions as a harmful factor for patient safety. Data analysis revealed three relevant categories: characteristics of interruptions, implications for patient safety, and interventions to minimize interruptions. CONCLUSION: interruptions favor the occurrence of errors in the health field. Therefore, there is a need for further studies to understand such a phenomenon and its effects on clinical practice.OBJETIVOS: identificar características relacionadas à interrupção de enfermeiros em sua prática profissional, bem como avaliar as implicações para a segurança do paciente. MÉTODO: foi realizada revisão de literatura do tipo integrativa, com busca nas bases de dados Pubmed/Medline, LILACS, SciELO e Biblioteca Cochrane, utilizando os descritores interruptions e patient safety. A data inicial não foi limitada e a data final foi 31 de dezembro de 2013, identificando-se 29 artigos que atenderam aos critérios de inclusão. RESULTADOS: todos os artigos revisados descreveram a interrupção como fator prejudicial à segurança do paciente. A análise destes estudos revelou três categorias relevantes: características da interrupção, implicações da interrupção para a segurança do paciente e intervenções para minimizar as interrupções. CONCLUSÃO: a interrupção favorece a ocorrência de erros na saúde. Assim, notou-se necessidade de novas pesquisas para compreender tal fenômeno e seus efeitos na prática clínica

Mapeamento de intervenções/atividades dos enfermeiros em centro quimioterápico: instrumento para avaliação da carga de trabalho

OBJETIVOS: identificar las intervenciones/actividades desarrolladas por enfermeros en un Centro Quimioterápico (CQT), utilizando lenguaje estandarizado, y validar su contenido. MÉTODO: fue utilizada triangulación de datos a través de la combinación de tres fuentes de informaciones: entrevista semiestructurada, análisis de documentos y cuestionario. El instrumento, construido en la taxonomía de la Clasificación de Intervenciones de Enfermería (NIC) fue sometido a la validación de contenido mediante reuniones con los participantes. RESULTADOS: Fueron mapeadas y validadas 35 intervenciones y 48 actividades organizadas en cinco dominios (fisiológico básico y fisiológico complexo, de la conducta, seguridad y sistema de salud) y 11 clases. CONCLUSIÓN: La identificación de las intervenciones/actividades del enfermero en CQT instrumentaliza la determinación del tiempo consumido y posibilita medir la carga de trabajo. También auxilia en la definición del papel de este profesional, posibilitando el rediseño del proceso de trabajo y optimizando la productividad.OBJETIVOS: identificar as intervenções/atividades desenvolvidas por enfermeiros em um centro quimioterápico, utilizando-se linguagem padronizada, e validar seu conteúdo. MÉTODO: utilizou-se triangulação de dados através da combinação de três fontes de informações: entrevista semiestruturada, análise de documentos e questionário. O instrumento, construído na taxonomia da Classificação de Intervenção de Enfermagem foi submetido à validação de conteúdo através de reuniões com os participantes. RESULTADOS: foram mapeadas e validadas 35 intervenções e 48 atividades organizadas em cinco domínios (fisiológico básico e fisiológico complexo, comportamental, segurança e sistema de saúde) e 11 classes. CONCLUSÃO: a identificação das intervenções/atividades do enfermeiro em centro quimioterápico instrumentaliza a determinação do tempo consumido e possibilita a mensuração da carga de trabalho, auxilia, também, na definição do papel desse profissional, possibilitando o redesenho do processo de trabalho e otimizando a produtividade.OBJECTIVES: identify the interventions/activities nurses develop at a Chemotherapy Center (CTC) using standardized language and validate their contents. METHOD: data triangulation was used through the combination of three information sources: semistructured interview, document analysis and questionnaire. The instrument, constructed in accordance with the Nursing Interventions Classification (NIC) taxonomy, was submitted to content validation through meetings with the participants. RESULTS: Thirty-five interventions and 48 activities were mapped and validated, organized in five domains (physiological: basic and physiological: complex, behavioral, safety and health system) and 11 classes. CONCLUSION: The identification of nurses' interventions/activities at CTC supports the determination of the time consumed and permits measuring the workload. It also helps to define these professionals' role, which permits the redesign of the work process and optimizes productivity

Machine learning and computational methods to identify molecular and clinical markers for complex diseases – case studies in cancer and obesity

In biomedical research, applied machine learning and bioinformatics are the essential disciplines heavily involved in translating data-driven findings into medical practice. This task is especially accomplished by developing computational tools and algorithms assisting in detection and clarification of underlying causes of the diseases. The continuous advancements in high-throughput technologies coupled with the recently promoted data sharing policies have contributed to presence of a massive wealth of data with remarkable potential to improve human health care. In concordance with this massive boost in data production, innovative data analysis tools and methods are required to meet the growing demand. The data analyzed by bioinformaticians and computational biology experts can be broadly divided into molecular and conventional clinical data categories. The aim of this thesis was to develop novel statistical and machine learning tools and to incorporate the existing state-of-the-art methods to analyze bio-clinical data with medical applications. The findings of the studies demonstrate the impact of computational approaches in clinical decision making by improving patients risk stratification and prediction of disease outcomes. This thesis is comprised of five studies explaining method development for 1) genomic data, 2) conventional clinical data and 3) integration of genomic and clinical data. With genomic data, the main focus is detection of differentially expressed genes as the most common task in transcriptome profiling projects. In addition to reviewing available differential expression tools, a data-adaptive statistical method called Reproducibility Optimized Test Statistic (ROTS) is proposed for detecting differential expression in RNA-sequencing studies. In order to prove the efficacy of ROTS in real biomedical applications, the method is used to identify prognostic markers in clear cell renal cell carcinoma (ccRCC). In addition to previously known markers, novel genes with potential prognostic and therapeutic role in ccRCC are detected. For conventional clinical data, ensemble based predictive models are developed to provide clinical decision support in treatment of patients with metastatic castration resistant prostate cancer (mCRPC). The proposed predictive models cover treatment and survival stratification tasks for both trial-based and realworld patient cohorts. Finally, genomic and conventional clinical data are integrated to demonstrate the importance of inclusion of genomic data in predictive ability of clinical models. Again, utilizing ensemble-based learners, a novel model is proposed to predict adulthood obesity using both genetic and social-environmental factors. Overall, the ultimate objective of this work is to demonstrate the importance of clinical bioinformatics and machine learning for bio-clinical marker discovery in complex disease with high heterogeneity. In case of cancer, the interpretability of clinical models strongly depends on predictive markers with high reproducibility supported by validation data. The discovery of these markers would increase chance of early detection and improve prognosis assessment and treatment choice

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics

Transcriptomics in Toxicogenomics, Part III : Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.Peer reviewe

Contemplating Mindfulness at Work: An Integrative Review

Mindfulness research activity is surging within organizational science. Emerging evidence across multiple fields suggests that mindfulness is fundamentally connected to many aspects of workplace functioning, but this knowledge base has not been systematically integrated to date. This review coalesces the burgeoning body of mindfulness scholarship into a framework to guide mainstream management research investigating a broad range of constructs. The framework identifies how mindfulness influences attention, with downstream effects on functional domains of cognition, emotion, behavior, and physiology. Ultimately, these domains impact key workplace outcomes, including performance, relationships, and well-being. Consideration of the evidence on mindfulness at work stimulates important questions and challenges key assumptions within management science, generating an agenda for future research

Cancer-Related Direct-To-Consumer Advertising - A Study of its Antecedents, Influence on Patient Information Seeking Behaviors, and Contingent Effects

Direct-to-consumer advertising (DTCA) of prescription medications and healthcare facilities has generated much debate over the potential benefits and adverse consequences for the public at large, patients, clinician-patient relationships, and the overall healthcare system. This dissertation is aimed at contributing to this debate through studying the impact of DTCA in the context of cancer treatment. Study 1 assessed the reliability and validity of three candidate measures of patient-reported exposure to cancer-related DTCA across seven criteria. The study found that all three measures performed well in terms of convergent, nomological, discriminant, and face validity. Findings from this study offer support for utilizing these survey measures in future studies targeting cancer patients. Next, Study 2 examined the prevalence and correlates of cancer-related DTCA exposure in a sample of patients in Pennsylvania diagnosed with breast, prostate, or colorectal cancers. On average, patients reported modest exposure to such DTCA (median exposure was once per week). Significant correlates of exposure included cancer type, age, stage of disease, and ethnicity. Study 3 investigated the relationships between DTCA exposure and subsequent information seeking behaviors. The analyses detected a significant association between DTCA exposure and cancer patients\u27 subsequent information engagement with their clinicians at one-year follow-up. Exposure to DTCA was marginally significant in predicting information seeking from non-clinician (lay media and interpersonal) sources. Based on the Integrative Model of Behavioral Prediction, a focused analysis showed a significant indirect path between DTCA exposure and subsequent information seeking from non-clinician sources, mediated through attitudes and intention to seek from these sources. Study 4 was guided by the Structural Influence Model of Communication to explore disparities in health information seeking behaviors arising from DTCA exposure. The study found that the associations between DTCA exposure and active information seeking behaviors were not moderated by patients\u27 age, educational level, race/ethnicity, or cancer type. To conclude, these studies would likely inform the ongoing debate and future research regarding the impact of cancer-related DTCA exposure on communication outcomes and disparities