Sixteen years of ICPC use in Norwegian primary care: looking through the facts

<p>Abstract</p> <p>Background</p> <p>The International Classification for Primary Care (ICPC) standard aims to facilitate simultaneous and longitudinal comparisons of clinical primary care practice within and across country borders; it is also used for administrative purposes. This study evaluates the use of the original ICPC-1 and the more complete ICPC-2 Norwegian versions in electronic patient records.</p> <p>Methods</p> <p>We performed a retrospective study of approximately 1.5 million ICPC codes and diagnoses that were collected over a 16-year period at 12 primary care sites in Norway. In the first phase of this period (transition phase, 1992-1999) physicians were allowed to not use an ICPC code in their practice while in the second phase (regular phase, 2000-2008) the use of an ICPC code was mandatory. The ICPC codes and diagnoses defined a problem event for each patient in the PROblem-oriented electronic MEDical record (PROMED). The main outcome measure of our analysis was the percentage of problem events in PROMEDs with inappropriate (or missing) ICPC codes and of diagnoses that did not map the latest ICPC-2 classification. Specific problem areas (pneumonia, anaemia, tonsillitis and diabetes) were examined in the same context.</p> <p>Results</p> <p>Codes were missing in 6.2% of the problem events; incorrect codes were observed in 4.0% of the problem events and text mismatch between the diagnoses and the expected ICPC-2 diagnoses text in 53.8% of the problem events. Missing codes were observed only during the transition phase while incorrect and inappropriate codes were used all over the 16-year period. The physicians created diagnoses that did not exist in ICPC. These 'new' diagnoses were used with varying frequency; many of them were used only once. Inappropriate ICPC-2 codes were also observed in the selected problem areas and for both phases.</p> <p>Conclusions</p> <p>Our results strongly suggest that physicians did not adhere to the ICPC standard due to its incompleteness, i.e. lack of many clinically important diagnoses. This indicates that ICPC is inappropriate for the classification of problem events and the clinical practice in primary care.</p

Visual Causality: Investigating Graph Layouts for Understanding Causal Processes

Causal diagrams provide a graphical formalism indicating how statistical models can be used to study causal processes. Despite the extensive research on the efficacy of aesthetic graphic layouts, the causal inference domain has not benefited from the results of this research. In this paper, we investigate the performance of graph visualisations for supporting users’ understanding of causal graphs. Two studies were conducted to compare graph visualisations for understanding causation and identifying confounding variables in a causal graph. The first study results suggest that while adjacency matrix layouts are better for understanding direct causation, node-link diagrams are better for understanding mediated causation along causal paths. The second study revealed that node-link layouts, and in particular layouts created by a radial algorithm, are more effective for identifying confounder and collider variables

Temporal Mapper: Transition networks in simulated and real neural dynamics

AbstractCharacterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method—Temporal Mapper—built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects’ behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics

Kinematic morphology of space systems

This thesis considers the robustness to faults of mechanical kinematic systems typical of the type applied to the locomotion sub-systems of planetary exploration vehicles. It is argued that, whereas the electronic, software and control methodologies for such kinematic systems have received extensive attention, the development of the theory supporting the corresponding mechanical architectures has not received the same level of attention. An introduction to the space systems context of the topic is provided, and used to illustrate the nature of the requirements that evolve for such missions, concentrating on aspects of'terrainability' - the suitability of a vehicle to manoeuvre on rough planetary surfaces. An approach is investigated which takes concepts from graph theory and linear algebra, and uses these to establish a means for representing kinematic topologies, and, in particular, the 'fault graph' structures, and 'fault classes' that result from the progressive application of faults to nominal kinematic system configurations. Ways whereby the eigenvalues and eigenvectors of the characteristic polynomials of the interchange graph adjacency matrices of various kinematic systems can be applied to represent such systems under nominal and fault conditions are investigated, including development of the 'constraints matrix'. Additionally, the relevance of entropy within a fault graph context is considered, and also techniques suggested for analysing systems in a way which allows a richer representation of the underlying kinematic structure. Various metrics are considered and a means is established whereby a selection of parameters representing some aspects of kinematic systems' behaviour is used in conjunction with these to provide a means of comparing system configurations with each other, in terms of several 'intersystem distance' measures. Some success was achieved - 'inter-system distances' were derived for a selection of systems exhibiting different topologies and showed that these can usefully be used to represent some aspects of kinematic system topologies. Some evidence was obtained that it is possible to discriminate between tree-based and looped systems using this method

Methods for longitudinal complex network analysis in neuroscience

The study of complex brain networks, where the brain can be viewed as a system with various interacting regions that produce complex behaviors, has grown tremendously over the past decade. With both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for dynamic brain network analysis are needed. We first propose a paradigm for longitudinal brain network analysis over patient cohorts where we adapt the Stochastic Actor Oriented Model (SAOM) framework and model a subject's network over time as observations of a continuous time Markov chain. Network dynamics are represented as being driven by various factors, both endogenous (i.e., network effects) and exogenous, where the latter include mechanisms and relationships conjectured in the literature. We outline an application to the resting-state fMRI network setting, where we draw conclusions at the subject level and then perform a meta-analysis on the model output. As an extension of the models, we next propose an approach based on Hidden Markov Models to incorporate and estimate type I and type II error (i.e., of edge status) in our observed networks. Our model consists of two components: 1) the latent model, which assumes that the true networks evolve according to a Markov process as they did in the original SAOM framework; and 2) the measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for estimation. Lastly, we focus on the study of percolation - the sudden emergence of a giant connected component in a network. This has become an active area of research, with relevance in clinical neuroscience, and it is of interest to distinguish between different percolation regimes in practice. We propose a method for estimating a percolation model from a given sequence of observed networks with single edge transitions. We outline a Hidden Markov Model approach and EM algorithm for the estimation of the birth and death rates for the edges, as well as the type I and type II error rates.2018-07-25T00:00:00

Effectiveness of a research-based reading development intervention in 2nd grade children

The purpose of this project is to develop a reading program for second grade children that is based on research. Reading development is a complex process impacted by a wide array of neurobiological, cognitive, social, and other related factors

Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples

Behavioural biometric identification based on human computer interaction

As we become increasingly dependent on information systems, personal identification and profiling systems have received an increasing interest, either for reasons of personali- sation or security. Biometric profiling is one means of identification which can be achieved by analysing something the user is or does (e.g., a fingerprint, signature, face, voice). This Ph.D. research focuses on behavioural biometrics, a subset of biometrics that is concerned with the patterns of conscious or unconscious behaviour of a person, involving their style, preference, skills, knowledge, motor-skills in any domain. In this work I explore the cre- ation of user profiles to be applied in dynamic user identification based on the biometric pat- terns observed during normal Human-Computer Interaction (HCI) by continuously logging and tracking the corresponding computer events. Unlike most of the biometrics systems that need special hardware devices (e.g. finger print reader), HCI-based identification sys- tems can be implemented using regular input devices (mouse or keyboard) and they do not require the user to perform specific tasks to train the system. Specifically, three components are studied in-depth: mouse dynamics, keystrokes dynamics and GUI based user behaviour. In this work I will describe my research on HCI-based behavioural biometrics, discuss the features and models I proposed for each component along with the result of experiments. In addition, I will describe the methodology and datasets I gathered using my LoggerMan application that has been developed specifically to passively gather behavioural biometric data for evaluation. Results show that normal Human-Computer Interaction reveals behavioural information with discriminative power sufficient to be used for user modelling for identification purposes

Computational optimization and prediction strategies for increasing communication rate in phoneme-based augmentative and alternative communication (AAC)

Up to 1.2% of the population is unable to meet daily communication needs using typical speech and may use augmentative and alternative communication (AAC) strategies to communicate, including manual sign language, facial gestures, and aided strategies such as selecting targets on an onscreen keyboard. However, for individuals whose impairments affect both speech and non-speech motor systems (e.g., spinal cord injury, amyotrophic lateral sclerosis, multiple sclerosis), their ability to use manual sign and access computer systems are impacted. AAC access methods in this population remain inherently slow and effortful (e.g., eye-tracking, head-tracking, mechanical switches). Thus, optimizing communication interfaces for alternate access methods may provide significant improvements in communication rates and quality of life. In this series of studies, we developed and evaluated methods for improving communication rates through optimization and prediction in communication interfaces. These interfaces enabled participants to select sounds (phonemes) instead of letters and were computationally optimized offline via a model of human movement in order for targets likely to be selected together to be in close proximity. Online prediction was implemented such that likely targets were dynamically enlarged. Computational simulations suggested that optimized phonemic interfaces could increase communication rates by up to 30.9% compared to random phonemic interfaces. Communication rates were empirically evaluated in 36 participants without motor impairment using an alternate computer access method to produce messages with phonemic interfaces over 12 sessions. Results suggested that optimization increased communication rates by 10.5–23.0% compared to a random phonemic interface. Prediction increased communication rates during training sessions, but was not a significant factor in communication rates during the final session. Empirical evaluations in individuals with motor impairment revealed that all participants strongly agreed that they would improve with practice, and four out of six participants strongly preferred the interface with prediction. Results of these studies suggest that optimized and predictive phonemic interfaces may provide increased communication rates for individuals with motor impairments affecting both oral communication and computer access. Methods for dynamically enlarging targets may also be applicable to other (non-phonemic) interfaces to increase communication rates. Further research is needed to fully translate these results into clinical practice.2020-10-24T00:00:00

Complex networks analysis in team sports performance: multilevel hypernetworks approach to soccer matches

Humans need to interact socially with others and the environment. These interactions lead to complex systems that elude naïve and casuistic tools for understand these explanations. One way is to search for mechanisms and patterns of behavior in our activities. In this thesis, we focused on players’ interactions in team sports performance and how using complex systems tools, notably complex networks theory and tools, can contribute to Performance Analysis. We began by exploring Network Theory, specifically Social Network Analysis (SNA), first applied to Volleyball (experimental study) and then on soccer (2014 World Cup). The achievements with SNA proved limited in relevant scenarios (e.g., dynamics of networks on n-ary interactions) and we moved to other theories and tools from complex networks in order to tap into the dynamics on/off networks. In our state-of-the-art and review paper we took an important step to move from SNA to Complex Networks Analysis theories and tools, such as Hypernetworks Theory and their structural Multilevel analysis. The method paper explored the Multilevel Hypernetworks Approach to Performance Analysis in soccer matches (English Premier League 2010-11) considering n-ary cooperation and competition interactions between sets of players in different levels of analysis. We presented at an international conference the mathematical formalisms that can express the players’ relationships and the statistical distributions of the occurrence of the sets and their ranks, identifying power law statistical distributions regularities and design (found in some particular exceptions), influenced by coaches’ pre-match arrangement and soccer rules.Os humanos necessitam interagir socialmente com os outros e com o envolvimento. Essas interações estão na origem de sistemas complexos cujo entendimento não é captado através de ferramentas ingénuas e casuísticas. Uma forma será procurar mecanismos e padrões de comportamento nas atividades. Nesta tese, o foco centra-se na utilização de ferramentas dos sistemas complexos, particularmente no contributo da teoria e ferramentas de redes complexas, na Análise do Desempenho Desportivo baseado nas interações dos jogadores de equipas desportivas. Começámos por explorar a Teoria das Redes, especificamente a Análise de Redes Sociais (ARS) no Voleibol (estudo experimental) e depois no futebol (Campeonato do Mundo de 2014). As aplicações da ARS mostraram-se limitadas (por exemplo, na dinâmica das redes em interações n-árias) o que nos trouxe a outras teorias e ferramentas das redes complexas. No capítulo do estadoda- arte e artigo de revisão publicado, abordámos as vantagens de utilização de outras teorias e ferramentas, como a análise Multinível e Teoria das Híperredes. No artigo de métodos, apresentámos a Abordagem de Híperredes Multinível na Análise do Desempenho em jogos de futebol (Premier League Inglesa 2010-11) considerando as interações de cooperação e competição nos conjuntos de jogadores, em diferentes níveis de análise. Numa conferência internacional, apresentámos os formalismos matemáticos que podem expressar as relações dos jogadores e as distribuições estatísticas da ocorrência dos conjuntos e a sua ordem, identificando regularidades de distribuições estatísticas de power law e design (encontrado nalgumas exceções estatísticas específicas), promovidas pelos treinadores na preparação dos jogos e constrangidas pelas regras do futebol