Identificação de questões críticas na utilização de big data no enquadramento regulamentar pré-AIM/pós-AIM

Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, 2020, Universidade de Lisboa, Faculdade de Farmácia.Num momento com o maior ritmo de mudança a que alguma vezes assistimos, torna-se urgente encontrar soluções que o permitam acompanhar. Numa época em que os dados são o “novo petróleo”, todos os que não souberem aproveitar as oportunidades vão ver as suas tentativas de evolução frustradas. A área da saúde não é exceção. Nunca, em qualquer outra época, tivemos acesso a tão grandes quantidades de dados. Estes, quando bem aproveitados, podem fornecer informações valiosas e gerar novo conhecimento. No entanto, novas oportunidades vêm sempre acompanhadas de desafios. O elevado volume de dados a que hoje temos acesso, exige o recurso a métodos e tecnologias analíticas avançados para que possamos adequadamente transformá-los em informação útil e fidedigna passível de ser utilizada. Sendo verdade em todas as áreas, é ainda mais evidente para a área da saúde, onde, o rigor e exigência de qualidade, são tão elevados quanto o próprio valor intrínseco que estes dados podem acrescentar.At a moment with the greatest pace of change we’ve ever seen it is urgent to find solutions that go along with it. At a time when data is the “new oil”, everyone who doesn't know how to take advantage of the opportunities will fail their evolution attempts. The health area is no exception. Never, in any other time, have we had such large amounts of data. When used properly, these can provide valuable information and generate new knowledge. However, new opportunities are always followed by challenges. The high volume of data to which we have access today, requires the use of advanced analytical methods and technologies for us to be able to transform them into useful and reliable information that can be used. Being true in all areas, it is even more evident for the health area, where the accuracy and demand for quality are as high as the intrinsic value that these data can add

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

Using the Literature to Identify Confounders

Prior work in causal modeling has focused primarily on learning graph structures and parameters to model data generating processes from observational or experimental data, while the focus of the literature-based discovery paradigm was to identify novel therapeutic hypotheses in publicly available knowledge. The critical contribution of this dissertation is to refashion the literature-based discovery paradigm as a means to populate causal models with relevant covariates to abet causal inference. In particular, this dissertation describes a generalizable framework for mapping from causal propositions in the literature to subgraphs populated by instantiated variables that reflect observational data. The observational data are those derived from electronic health records. The purpose of causal inference is to detect adverse drug event signals. The Principle of the Common Cause is exploited as a heuristic for a defeasible practical logic. The fundamental intuition is that improbable co-occurrences can be “explained away” with reference to a common cause, or confounder. Semantic constraints in literature-based discovery can be leveraged to identify such covariates. Further, the asymmetric semantic constraints of causal propositions map directly to the topology of causal graphs as directed edges. The hypothesis is that causal models conditioned on sets of such covariates will improve upon the performance of purely statistical techniques for detecting adverse drug event signals. By improving upon previous work in purely EHR-based pharmacovigilance, these results establish the utility of this scalable approach to automated causal inference

Additional file 1: Table S1. of Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams

The list of common drug names used as an initial filter for the Twitter-stream. Table S2. Terms that frequently caused erroneous mappings from the MetaMap entity extraction system. (DOCX 23 kb