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Discovering unknown adverse drug reactions (ADRs) as early as possible is highly desirable. Current methods largely rely on passive spontaneous reports, which suffer from serious underreporting, latency, and inconsistent reporting. They are not ideal for early identification of ADRs. In this paper, we propose a multi-agent system approach for ADR detection. A multi-agent system is formed by a community...
Adverse Drug Events (ADEs) are currently considered as a major public health issue, resulting in endangering patients' safety and significant healthcare costs. The EU-funded project PSIP (Patient Safety through Intelligent Procedures in Medication) aims to develop intelligent mechanisms towards preventing ADEs, aiming to improve the entire Prescription - Dispensation - Administration - Compliance...
The master/worker pattern is widely used to construct the cross-domain, large scale computing infrastructure. The applications supported by this kind of infrastructure usually features long-running, speculative execution etc. Fault recovery mechanism is significant to them especially in the wide area network environment, which consists of error prone components. Inter-node cooperation is urgent to...
The WebInVivo project aims at providing automated support for clinical research on neglected diseases. It includes mechanisms for (a) sharing and reusing clinical trial assets, such as protocols, protocol data, workflows and workflow metadata and (b) controlling the protocol life cycle, from modelling to execution. In this project, collaboration in the biomedical area will permeate three segments...
This paper presents an approach for a Q-learning based decision support system for therapy planning. It focuses the consideration on a multilevel approach for constructing a data driven evidence based model for classification of different drug dosages and their effectiveness for clinical trials. We consider time-ordered sequences of patient data (critical patient events) called patient trials consisting...
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