EA3: A softmax algorithm for evidence appraisal aggregation

Fast Methods for Drug Approval: Research Perspectives for Pandemic Preparedness

Public heath emergencies such as the outbreak of novel infectious diseases represent a major challenge for drug regulatory bodies, practitioners, and scientific communities. In such critical situations drug regulators and public health practitioners base their decisions on evidence generated and synthesised by scientists. The urgency and novelty of the situation create high levels of uncertainty concerning the safety and effectiveness of drugs. One key tool to mitigate such emergencies is pandemic preparedness. There seems to be, however, a lack of scholarly work on methodology for assessments of new or existing drugs during a pandemic. Issues related to risk attitudes, evidence production and evidence synthesis for drug approval require closer attention. This manuscript, therefore, engages in a conceptual analysis of relevant issues of drug assessment during a pandemic. To this end, we rely in our analysis on recent discussions in the philosophy of science and the philosophy of medicine. Important unanswered foundational questions are identified and possible ways to answer them are considered. Similar problems often have similar solutions, hence studying similar situations can provide important clues. We consider drug assessments of orphan drugs and drug assessments during endemics as similar to drug assessment during a pandemic. Furthermore, other scientific fields which cannot carry out controlled experiments may guide the methodology to draw defeasible causal inferences from imperfect data. Future contributions on methodologies for addressing the issues raised here will indeed have great potential to improve pandemic preparedness.

Application for Recognizing Sign Language Gestures Based on an Artificial Neural Network

This paper presents the development and implementation of an application that recognizes American Sign Language signs with the use of deep learning algorithms based on convolutional neural network architectures. The project implementation includes the development of a training set, the preparation of a module that converts photos to a form readable by the artificial neural network, the selection of the appropriate neural network architecture and the development of the model. The neural network undergoes a learning process, and its results are verified accordingly. An internet application that allows recognition of sign language based on a sign from any photo taken by the user is implemented, and its results are analyzed. The network effectiveness ratio reaches 99% for the training set. Nevertheless, conclusions and recommendations are formulated to improve the operation of the application.

E-synthesis for carcinogenicity assessments: A case study of processed meat

RATIONALE, AIMS AND OBJECTIVES

Recent controversies about dietary advice concerning meat demonstrate that aggregating the available evidence to assess a putative causal link between food and cancer is a challenging enterprise.

METHODS

We show how a tool developed for assessing putative causal links between drugs and adverse drug reactions, E-Synthesis, can be applied for food carcinogenicity assessments. The application is demonstrated on the putative causal relationship between processed meat consumption and cancer.

RESULTS

The output of the assessment is a Bayesian probability that processed meat consumption causes cancer. This Bayesian probability is calculated from a Bayesian network model, which incorporates a representation of Bradford Hill's Guidelines as probabilistic indicators of causality. We show how to determine probabilities of indicators of causality for food carcinogenicity assessments based on assessments of the International Agency for Research on Cancer.

CONCLUSIONS

We find that E-Synthesis is a tool well-suited for food carcinogenicity assessments, as it enables a graphical representation of lines and weights of evidence, offers the possibility to make a great number of judgements explicit and transparent, outputs a probability of causality suitable for decision making and is flexible to aggregate different kinds of evidence.

A smart hospital-driven approach to precision pharmacovigilance

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.