Influence of Seasonality and Public-Health Interventions on the COVID-19 Pandemic in Northern Europe.

Background: Most government efforts to control the COVID-19 pandemic revolved around non-pharmaceutical interventions (NPIs) and vaccination. However, many respiratory diseases show distinctive seasonal trends. In this manuscript, we examined the contribution of these three factors to the progression of the COVID-19 pandemic.

Attitudes towards technology supported rheumatoid arthritis care: investigating patient- and clinician-perceived opportunities and barriers.

Objectives: Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end users. This study was designed to understand existing challenges, opportunities and barriers for computer technology in the RA care pathway.

Categorical Updating in a Bayesian Propensity Problem.

We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent-based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations.

A causal Bayesian network approach for consumer product safety and risk assessment.

Introduction: Product risk assessment is the overall process of determining whether a product is judged safe for consumers to use. Among several methods for product risk assessment, RAPEX is the primary one used by regulators in the UK and EU. Despite its widespread use we identify several limitations of RAPEX, including a limited approach to handling uncertainty, especially in the absence of data, and the inability to incorporate causal explanations for using and interpreting the data.

mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review.

Aims: Gestational diabetes (GDM) is the most common metabolic disorder of pregnancy, requiring complex management and empowerment of those affected. Mobile health (mHealth) applications (apps) are proposed for streamlining healthcare service delivery, extending care relationships into the community, and empowering those affected by prolonged medical disorders to be equal collaborators in their healthcare. This review investigates mHealth apps intended for use with GDM; specifically those powered by artificial intelligence (AI) or providing decision support.

A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future.

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123.

Bayesian networks in healthcare: What is preventing their adoption?

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare.

Current review and next steps for artificial intelligence in multiple sclerosis risk research.

In the last few decades, the prevalence of multiple sclerosis (MS), a chronic inflammatory disease of the nervous system, has increased, particularly in Northern European countries, the United States, and United Kingdom. The promise of artificial intelligence (AI) and machine learning (ML) as tools to address problems in MS research has attracted increasing interest in these methods. Bayesian networks offer a clear advantage since they can integrate data and causal knowledge allowing for visualizing interactions between dependent variables and potential confounding factors.

Explaining Away, Augmentation, and the Assumption of Independence.

In reasoning about situations in which several causes lead to a common effect, a much studied and yet still not well-understood inference is that of Assuming that the causes contribute independently to the effect, if we learn that the effect is present, then this increases the probability that one or more of the causes are present. But if we then learn that a particular cause is present, this cause "explains" the presence of the effect, and the probabilities of the other causes decrease again. People tend to show this explaining away effect in their probability judgments, but to a lesser extent than predicted by the causal structure of the situation.

Propensities and Second Order Uncertainty: A Modified Taxi Cab Problem.

The study of people's ability to engage in causal probabilistic reasoning has typically used fixed-point estimates for key figures. For example, in the classic taxi-cab problem, where a witness provides evidence on which of two cab companies (the more common 'green'/less common 'blue') were responsible for a hit and run incident, solvers are told the witness's ability to judge cab color is 80%. In reality, there is likely to be some uncertainty around this estimate (perhaps we tested the witness and they were correct 4/5 times), known as second-order uncertainty, producing a distribution rather than a fixed probability.

Learning from Behavioural Changes That Fail.

Behavioural change techniques are currently used by many global organisations and public institutions. The amassing evidence base is used to answer practical and scientific questions regarding what cognitive, affective, and environment factors lead to successful behavioural change in the laboratory and in the field. In this piece we show that there is also value to examining interventions that inadvertently fail in achieving their desired behavioural change (e.

Bayesian networks in healthcare: Distribution by medical condition.

Bayesian networks (BNs) have received increasing research attention that is not matched by adoption in practice and yet have potential to significantly benefit healthcare. Hitherto, research works have not investigated the types of medical conditions being modelled with BNs, nor whether there are any differences in how and why they are applied to different conditions. This research seeks to identify and quantify the range of medical conditions for which healthcare-related BN models have been proposed, and the differences in approach between the most common medical conditions to which they have been applied.

Medical idioms for clinical Bayesian network development.

Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement.

Dependencies in evidential reports: The case for informational advantages.

Whether assessing the accuracy of expert forecasting, the pros and cons of group communication, or the value of evidence in diagnostic or predictive reasoning, dependencies between experts, group members, or evidence have traditionally been seen as a form of redundancy. We demonstrate that this conception of dependence conflates the structure of a dependency network, and the observations across this network. By disentangling these two elements we show, via mathematical proof and specific examples, that there are cases where dependencies yield an informational advantage over independence.

Data Visualisation in Midwifery: The Challenge of Seeing what Datasets Hide.

Information visualisation is transforming data into visual representations to convey information hidden within large datasets. Information visualisation in medicine is underdeveloped. In midwifery, the impact of different graphs on clinicians' and patients' understanding is not well understood.

Towards standardisation of evidence-based clinical care process specifications.

There is a strong push towards standardisation of treatment approaches, care processes and documentation of clinical practice. However, confusion persists regarding terminology and description of many clinical care process specifications which this research seeks to resolve by developing a taxonomic characterisation of clinical care process specifications. Literature on clinical care process specifications was analysed, creating the starting point for identifying common characteristics and how each is constructed and used in the clinical setting.

A framework for analysing learning health systems: Are we removing the most impactful barriers?

Introduction: Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption.

LAGOS: learning health systems and how they can integrate with patient care.

Problem: Learning health systems (LHS) are an underexplored concept. How LHS will operate in clinical practice is not well understood. This paper investigates the relationships between LHS, clinical care process specifications (CCPS) and the established levels of medical practice to enable LHS integration into daily healthcare practice.

Resolving the so-called "probabilistic paradoxes in legal reasoning" with Bayesian networks.

Examples of reasoning problems such as the twins problem and poison paradox have been proposed by legal scholars to demonstrate the limitations of probability theory in legal reasoning. Specifically, such problems are intended to show that use of probability theory results in legal paradoxes. As such, these problems have been a powerful detriment to the use of probability theory - and particularly Bayes theorem - in the law.

Assessment of the methodological quality of local clinical practice guidelines on the identification and management of gestational diabetes.

Objective: Gestational diabetes is the most common metabolic disorder of pregnancy, and it is important that well-written clinical practice guidelines (CPGs) are used to optimise healthcare delivery and improve patient outcomes. The aim of the study was to assess the methodological quality of hospital-based CPGs on the identification and management of gestational diabetes.

Design: We conducted an assessment of local clinical guidelines in English for gestational diabetes using the Appraisal of Guidelines for Research and Evaluation (AGREE II) to assess and validate methodological quality.

Analyzing the Simonshaven Case Using Bayesian Networks.

This paper is one in a series of analyses of the Dutch Simonshaven murder case, each using a different modeling approach. We adopted a Bayesian network (BN)-based approach which requires us to determine the relevant hypotheses and evidence in the case and their relationships (captured as a directed acyclic graph) along with explicit prior conditional probabilities. This means that both the graph structure and probabilities had to be defined using subjective judgments about the causal, and other, connections between variables and the strength and nature of the evidence.

Modelling competing legal arguments using Bayesian model comparison and averaging.

Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and 'average' Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization.

The Zero-Sum Fallacy in Evidence Evaluation.

There are many instances, both in professional domains such as law, forensics, and medicine and in everyday life, in which an effect (e.g., a piece of evidence or event) has multiple possible causes.

The Heimdall Framework for Supporting Characterisation of Learning Health Systems.

Background: Learning Health Systems (LHS) can focus population medicine and Evidence Based Practice; smart technology delivering the next generation of improved healthcare described as Precision Medicine, and yet researchers in the LHS domain presently lack the ability to recognise their relevant works as falling within this domain.

Objective: To review LHS literature and develop a framework describing the domain that can be used as a tool to analyse the literature and support researchers to identify health informatics investigations as falling with the domain of LHS.

Method: A scoping review is used to identify literature on which analysis was performed.