An Optimal Approach for Mining Rare Causal Associations to Detect ADR Signal Pairs

Abstract- Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited premarketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further, developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined. Index Terms- In this paper, we try to employ a knowledgebased approach to capture the degree of causality of an event pair within each sequence and we are going to match the data which was previously referred or suggested for treatment. � It is majorly used for Immediate Treatment for patients. However, mining the relationships between Drug and its Signal Reaction will be treated by In-Experienced Physician’

A multi-agent intelligent system for detecting unknown adverse drug reactions through communication and collaboration

Several thousands of drugs are currently available on the U.S. market. A complete understanding of the safe use of drugs is not possible at the time when drug is developed or marketed. At that time, the safety information is only obtained from a few thousand people in a typical pre-marketing clinical trial. Clinical trials are not capable of detecting rare adverse drug reactions (ADRs) because of limitations in sample size and trial duration. Early detection of unknown ADRs could save lives and prevent unnecessary hospitalizations. Current methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare ADRs. In this dissertation, I developed a team-based multi-agent intelligent system approach for proactively detecting potential ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions). The basic idea is that intelligent agents are capable of collaborating with one another by sharing information and knowledge which will accelerate the process of detecting ADR signal pairs. Each agent is equipped with a fuzzy inference engine, which enables it to find the causal relationship between a drug and a potential ADR (i.e., a signal pair). The fuzzy inference uses detection rules developed by me in this dissertation. The detection rules are based on different factors. I have also developed a methodology to find similar patients in the multi-agents system. The developed methodology uses similarity fuzzy rules in order to find similar patients in each agent\u27s patient database. In this dissertation, I developed a cooperative learning mechanism that was used by the agents in identifying ADR signal pairs and finding similar patients. The basic idea is that the agents are capable of collaborating with one another by sharing their knowledge. The agents start collaboration by providing their knowledge (i.e. rules) to the other agents. Using confidence level, the most important and insightful detection rules will be found and used for the benefit of the entire agent system. The new updated rules will lead to improve the agents\u27 decision performance. To evaluate our approach, I designed a four-agent system and implemented it using JADE and FuzzyJess software packages. I choose four because it is representative enough while computing time is still reasonable. To assess the performance of the developed system, I conducted two simulation experiments that involved over 20,000 patients treated at the Veterans Affairs Medical Center in Detroit between 2005 and 2008. From the software standpoint, the four agents collaboratively worked one another as designed. Two physicians on the team independently reviewed the multi-agent system results. The results indicate that the agents can successfully collaborate in finding ADR signal pairs and finding similar patients

A theoretical and practical approach to a persuasive agent model for change behaviour in oral care and hygiene

There is an increased use of the persuasive agent in behaviour change interventions due to the agent‘s features of sociable, reactive, autonomy, and proactive. However, many interventions have been unsuccessful, particularly in the domain of oral care. The psychological reactance has been identified as one of the major reasons for these unsuccessful behaviour change interventions. This study proposes a formal persuasive agent model that leads to psychological reactance reduction in order to achieve an improved behaviour change intervention in oral care and hygiene. Agent-based simulation methodology is adopted for the development of the proposed model. Evaluation of the model was conducted in two phases that include verification and validation. The verification process involves simulation trace and stability analysis. On the other hand, the validation was carried out using user-centred approach by developing an agent-based application based on belief-desire-intention architecture. This study contributes an agent model which is made up of interrelated cognitive and behavioural factors. Furthermore, the simulation traces provide some insights on the interactions among the identified factors in order to comprehend their roles in behaviour change intervention. The simulation result showed that as time increases, the psychological reactance decreases towards zero. Similarly, the model validation result showed that the percentage of respondents‘ who experienced psychological reactance towards behaviour change in oral care and hygiene was reduced from 100 percent to 3 percent. The contribution made in this thesis would enable agent application and behaviour change intervention designers to make scientific reasoning and predictions. Likewise, it provides a guideline for software designers on the development of agent-based applications that may not have psychological reactance

Análise das notificações de queixas técnicas e das medidas preventivas de fiscalização sanitária, aplicadas durante a pandemia da COVID-19 no Brasil

Introdução: O consumo de medicamentos falsificados, não registrados e fora do padrão é um grave problema de saúde pública, sendo fundamental conhecer as ações legais de fiscalização sanitária durante a pandemia no Brasil. Objetivo: Analisar as notificações de queixas técnicas e as medidas preventivas de fiscalização sanitária, aplicadas durante a pandemia da COVID-19, no Brasil. Método: Estudo descritivo, quantitativo, retrospectivo com dados das notificações de queixas técnicas e das medidas determinadas pela Agência Nacional de Vigilância Sanitária (Anvisa), no intervalo de 2019 a junho de 2021. As notificações foram obtidas por meio da Lei de Acesso à Informação, enquanto as medidas preventivas foram coletadas no portal da Anvisa, com a validação dos dados no site da imprensa nacional. Resultados: Foram registradas 25.088 notificações de queixas técnicas de medicamentos e 562 medidas, sendo 314 com elevado potencial de causar sérios danos à saúde, incluindo óbito. A maioria dos produtos não registrados alegam propriedades farmacêuticas relativas à imagem corporal, à imunidade ou ditos “naturais” ou da Medicina Tradicional Chinesa. Dentre as medidas, 63,3% estavam voltadas para propaganda irregular e comércio eletrônico de medicamentos suspeitos. Em cerca de 59,3% das medidas, as empresas não tinham o Cadastro Nacional de Pessoa Jurídica na Receita Federal, o que dificulta a responsabilização legal do infrator. Quanto às ações de fiscalização, as mais frequentes foram: apreensão (322), inutilização (305) e proibição (376). Quanto às medidas de proibição, as mais citadas se relacionam à pós-produção no ciclo produtivo, incluindo distribuição, comercialização e uso. Observa-se que a frequência observada de medicamentos segundo a classificação de risco é significativamente diferente para as várias ações fiscalizatórias (p < 0,001). Conclusões: Faz-se necessário desenvolver estratégias voltadas à prevenção, detecção e resposta às práticas irregulares/ilícitas, mediante revisão do marco regulatório e do modelo de atuação; maior responsabilização das empresas; mecanismos de rastreabilidade; ações fiscalizatórias efetivas e aprimoramento das medidas adotadas pela Anvisa para proteger a saúde da população

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Breakthroughs and emerging insights from ongoing design science projects: Research-in-progress papers and poster presentations from the 11th international conference on design science research in information systems and technology (DESRIST) 2016. St. John, Newfoundland, Canada, May 23-25

This volume contains selected research-in-progress papers and poster presentations from DESRIST 2016 - the 11th International Conference on Design Science Research in Information Systems and Technology held during 24-25 May 2016 at St. John's, Newfoundland, Canada. DESRIST provides a platform for researchers and practitioners to present and discuss Design Science research. The 11th DESRIST built on the foundation of ten prior highly successful international conferences held in Claremont, Pasadena, Atlanta, Philadelphia, St. Gallen, Milwaukee, Las Vegas, Helsinki, Miami, and Dublin. This year's conference places a special emphasis on using Design Science to engage with the growing challenges that face society, including (but not limited to) demands on health care systems, climate change, and security. With these challenges in mind, individuals from academia and industry came together to discuss important ongoing work and to share emerging knowledge and ideas. Design Science projects often involve multiple sub-problems, meaning there may be a delay before the final set of findings can be laid out. Hence, this volume "Breakthroughs and Observations from Ongoing Design Science Projects" presents preliminary findings from studies that are still underway. Completed research from DESRIST 2016 is presented in a separate volume entitled "Tackling Society's Grand Challenges with Design Science", which is published by Springer International Publishing, Switzerland. The final set of accepted papers in this volume reflects those presented at DESRIST 2016, including 11 research-in-progress papers and 4 abstracts for poster presentations. Each research-in-progress paper and each poster abstract was reviewed by a minimum of two referees. We would like to thank the authors who submitted their research-in-progress papers and poster presentations to DESRIST 2016, the referees who took the time to construct detailed and constructive reviews, and the Program Committee who made the event possible. Furthermore we thank the sponsoring organisations, in particular Maynooth University, Claremont Graduate University, and Memorial University of Newfoundland, for their financial support. We believe the research described in this volume addresses some of the most topical and interesting design challenges facing the field of information systems. We hope that readers find the insights provided by authors as valuable and thought-provoking as we have, and that the discussion of such early findings can help to maximise their impact

Regulatory Sandboxes and the Public Health

Recently, administrative agencies around the world have engaged in a grand experiment to regulate new technologies: regulatory sandboxes. Regulatory sandboxes allow developers, in cooperation with an agency, to conduct limited tests of new technologies in real-world settings for the pur- pose of generating and sharing information about them. Thus far, however, “regulatory sandboxes”—as named—appear almost exclusively in the con- text of financial technologies, or FinTech. Whether regulatory sandboxes, in fact, exist elsewhere in administrative law would be a significant finding for both regulators and scholars; it would blunt criticisms that agencies are slow to respond to new technologies, provide regulators with an additional tool for governing new technologies, and suggest that lessons learned from current regulatory sandboxes are applicable elsewhere. This Article is the first to explore this broader view of regulatory sand- boxes and develop a synoptic theory of them. To do so, it uses one of the most radical programs to introduce new technologies in U.S. history: the U.S. Food and Drug Administration’s (“FDA”)’s Emergency Use Authorization (“EUA”) program for COVID-19 treatments and vaccines. EUAs—like regulatory sandboxes but in stark contrast to typical FDA approval processes—focus on real-world deployment as a means for information gathering. EUAs are also technologically flexible and crafted with close input from the developer, among other features. Generalizing FDA’s experience with EUAs also provides lessons about the intersection of regulatory sandboxes with public trust in the agency, political interference, and the maintenance of regulatory standards. At the same time, FDA’s COVID-19 EUAs are exceptional in two senses: they touch upon the public health, widely considered to be exceptional subject matter in administrative law; and arose in the context of an unprecedented global pandemic. Nonetheless, FDA’s experience with EUAs suggest regulatory sandboxes may be an underexplored and undertheorized feature of administrative governance of new technologies. Future work in the area should assess whether regulatory sandboxes exist under the rubric identified here, which technologies they regulate, and how those sandboxes operate.Ope

Communicating post-market safety risks of medicines with regulatory safety advisories: an international comparison of policy and perceptions

Background Information about the safety of medicines often emerges after approval. Medicines’ regulators use post-market safety advisories to communicate potential new harms. Advisories can influence medicines use, helping users to weigh benefits and harms. This thesis compared regulatory policy and outcomes for post-market safety communication in Australia, Canada, the United Kingdom (as part of the European Union) and the United States (US). Methods The four regulators were compared using: • A regulatory policy analysis. • An in-depth case study of safety communications for SGLT2 inhibitors (2012-2018). • A content analysis of safety advisories issued for new drugs approved in Australia 2010-2016. • Qualitative interviews exploring prescriber awareness and use of medicines safety information (Boston and Australia). Results Differences in regulatory policy among the European Medicines Agency, the US Food and Drug Administration, Health Canada, and the Therapeutic Goods Administration (TGA) included: their legislated authority for safety advisories, transparency, and interactions with pharmaceutical industry. SGLT2 inhibitor safety advice differed among regulators in number, timing, and strength. TGA advisories were issued for 20.5% of 73 safety concerns communicated by other regulators, for new drugs approved in Australia (2010-2016). Differences were not explained by the seriousness of safety concerns. Prescribers’ awareness of regulatory safety advisories was relatively low, particularly in Australia. While respecting regulators’ institutional authority, regulatory warnings may lack clinical authority. Conclusions There are considerable differences amongst the EMA, FDA, Health Canada and the TGA in policy and use of post-market safety advisories. Recommendations for improving safety and policy are discussed

Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service

A study of multiple perspectives and knowledge in adverse drug reaction decision-making : Volume 1

Injury and illness associated with drugs are major problems in Australia and around the world, despite significant developments in the area of adverse drug reaction (ADR) decision support technology. The aims of this thesis are: to investigate the ADR decision domain; to determine factors that may assist in the prevention, detection and management of ADRs; and, to inform the pre-requirements analysis phase of the development of decision support systems. An approach has been taken that permits open and grounded study of the decision environment. This approach can then be used to frame and inform the design of an ADR decision support system. Fifteen case studies that comprise self selected consumers, the treating medical practitioner/s and expert perspectives of a single instance of an ADR (fifteen in-depth consumer interviews, eight in-depth medical practitioner interviews and 30 expert written questionnaires), have been collected and analysed using a grounded theory approach, a symbolic interactionist theoretical framework and a social constructionist epistemology. The analysis was performed from three perspectives: individual case study analysis (all interviews for an instance of an ADR); group analysis (consumer, medical practitioner and expert views) and analysis combining the individual case studies and groups of data. Concepts, themes and theory have emerged from these data in the following areas: • the contribution of the differences in understanding of the core concepts within this domain, to misunderstandings between decision-makers; • the consumer as a diagnostic decision-maker in the ADR decision domain; • differential diagnostic strategies used by the consumers and medical practitioners; • complexities in the ADR decision domain that make diagnosis difficult; • the role of ADR information in consumer and medical practitioner decision-making; • decision types used by consumers and medical practitioners in the ADR decision domain; • resources used by consumers, medical practitioners and experts to inform their ADR decisions; • decision-making with partial knowledge of the consumer case history, drug behaviour and diseases; • the impact of suspected ADRs on consumers and on future decision-making; • medical practitioner/consumer decision-making models; and, • reasons for low ADR reporting and the impact on the development of new ADR knowledge. The results above suggest the following: • The ADR decision domain is more complex than the current ADR decision support focus and that broadening this focus may assist in providing a more complete and useful decision support solution. • Improving the prevention, detection and management of ADRs requires more than providing prescribers with up to date ADR information. Other important factors are sharing of information, awareness of the role of the consumer, a collaborative approach between the consumers and medical practitioners, and generation of new ADR knowledge. • A grounded theory analysis of case study data using the theoretical perspectives of social constructionism and symbolic interactionism provided insight into this domain from the perspectives of multiple decision-makers. This may be an approach that can be used by systems analysts to inform the requirements analysis phases of decision support within other domains. The results of this qualitative work are preliminary. Future work is required to confirm and expand these results.Doctor of Philosoph