Supporting physicians and patients through recommendation: Guidelines and beyond

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Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

How to Read the Book “Foundations of Biomedical Knowledge Representation”

Biology and medicine are very rich knowledge domains in which already at an early stage in their scientific development it was realised that without a proper way to organise this knowledge they would inevitably turn into chaos. Early examples of organisation attempts are for example “De Rerum Natura (On the Nature of Things)” by Titus Lucretius Carus (99–55 BC), which explains the natural and physical world as known at the time, and of course the work “Systema Naturae” by Carl Linnaeus published in 1735. The latter book can be seen as the clear recognition of the need of using systematic methods, here principles of taxonomic organisation, to classify nature. As soon as one considers using systematic methods, computer-based representations and algorithms come to mind

Logic and the Quality of Medical Guidelines

Requirements about the quality of medical guidelines can be represented using schemata borrowed from the theory of abductive diagnosis, using temporal logic to model the time-oriented aspects expressed in a guideline. In this paper, we investigate how this approach can be mapped to the facilities offered by a theorem proving system for program verification, KIV. It is shown that the reasoning that is required for checking the quality of a guideline can be mapped to such theorem-proving facilities.

Denial-of-Service Attacks on LoRaWAN

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Approximate Probabilistic Inference with Bounded Error for Hybrid Probabilistic Logic Programming

Contains fulltext : 159584.pdf (publisher's version ) (Open Access

Exploring disease interactions using Markov networks

Network medicine is an emerging paradigm for studying the co-occurrence between diseases. While diseases are often interlinked through complex patterns, most of the existing work in this area has focused on studying pairwise relationships between diseases. In this paper, we use a state-of-the-art Markov network learning method to learn interactions between musculoskeletal disorders and cardiovascular diseases and compare this to pairwise approaches. Our experimental results confirm that the sophisticated structure learner produces more accurate models, which can help reveal interesting patterns in the co-occurrence of diseases.status: publishe

Measuring adverse drug effects on multimorbity using tractable Bayesian networks

Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseases are typically unknown. In particular, drugs prescribed for one condition may result in adverse effects for the other. To investigate which types of drugs may affect the further progression of multimorbidity, we query models of diseases and prescriptions that are learned from primary care data. State-of-the-art tractable Bayesian network representations, on which such complex queries can be computed efficiently, are employed for these large medical networks. Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases. Moreover, a drug treatment for one disease group may affect diseases of another group.status: publishe