Evolution and challenges in the design of computational systems for triage assistance

AbstractCompared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems

Modelado de sistema experto para triaje en servicios de urgencias médicas

Los modelos gráficos probabilísticos, tales como las redes bayesianas y los diagramas de influencias permiten representar de forma coherente el conocimiento de un dominio bajo condiciones de incertidumbre. Están basados en los fundamentos de la teoría de la probabilidad y permiten combinar el juicio del experto con las fuentes de datos disponibles. Este articulo describe el trabajo actual que estamos realizando para la aplicación de redes bayesianas en el modelado de sistemas expertos de triaje (clasificación) en los servicios de urgencias médicas. Las redes son construidas teniendo en cuenta tanto los datos provenientes de experiencias de triaje como la opinión de médicos expertos en urgencias. El sistema será utilizado con una doble finalidad: a nivel teórico para entender cómo la información requerida en el triaje puede ser modelada mediante redes bayesianas y a nivel práctico para entrenamiento y uso por el personal de triaje.The Probabilistic graphical models, such as the bayesian networks and the diagrams of influences allow to represent of coherent form the knowledge of a dominion under conditions of uncertainty. They are based on the foundations of the theory of the probability and allow to combine the judgment of the expert with the sources of data available. This article describes the present work that we are making for the application of bayesian networks in the modeled one of expert systems of triage (classification) in the services of medical urgencies. The networks are constructed considering as much the originating data of experiences of triage like the opinion of expert doctors in urgencies. The system will be used with one double purpose: at theoretical level to understand how the information required in the triage can be modeled by means of Bayesian networks and at practical level for training and use by the triage personnel.Red de Universidades con Carreras en Informática (RedUNCI

Aplicación de redes bayesianas en el modelado de un sistema experto de triaje en servicios de urgencias médicas

Este articulo describe el trabajo actual que estamos realizando para la aplicación de redes bayesianas en el modelado de sistemas expertos de triaje (clasificación) en los servicios de urgencias médicas. Las redes son construidas teniendo en cuenta tanto los datos provenientes de experiencias de triaje como la opinión de médicos expertos en urgencias. El sistema será utilizado con una doble finalidad: a nivel teórico para entender cómo la información requerida en el triaje puede ser modelada mediante redes bayesianas y a nivel práctico para entrenamiento y uso por el personal de triaje.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Modelado de sistema experto para triaje en servicios de urgencias médicas

Los modelos gráficos probabilísticos, tales como las redes bayesianas y los diagramas de influencias permiten representar de forma coherente el conocimiento de un dominio bajo condiciones de incertidumbre. Están basados en los fundamentos de la teoría de la probabilidad y permiten combinar el juicio del experto con las fuentes de datos disponibles. Este articulo describe el trabajo actual que estamos realizando para la aplicación de redes bayesianas en el modelado de sistemas expertos de triaje (clasificación) en los servicios de urgencias médicas. Las redes son construidas teniendo en cuenta tanto los datos provenientes de experiencias de triaje como la opinión de médicos expertos en urgencias. El sistema será utilizado con una doble finalidad: a nivel teórico para entender cómo la información requerida en el triaje puede ser modelada mediante redes bayesianas y a nivel práctico para entrenamiento y uso por el personal de triaje.The Probabilistic graphical models, such as the bayesian networks and the diagrams of influences allow to represent of coherent form the knowledge of a dominion under conditions of uncertainty. They are based on the foundations of the theory of the probability and allow to combine the judgment of the expert with the sources of data available. This article describes the present work that we are making for the application of bayesian networks in the modeled one of expert systems of triage (classification) in the services of medical urgencies. The networks are constructed considering as much the originating data of experiences of triage like the opinion of expert doctors in urgencies. The system will be used with one double purpose: at theoretical level to understand how the information required in the triage can be modeled by means of Bayesian networks and at practical level for training and use by the triage personnel.Red de Universidades con Carreras en Informática (RedUNCI

Aplicación de redes bayesianas en el modelado de un sistema experto de triaje en servicios de urgencias médicas

Este articulo describe el trabajo actual que estamos realizando para la aplicación de redes bayesianas en el modelado de sistemas expertos de triaje (clasificación) en los servicios de urgencias médicas. Las redes son construidas teniendo en cuenta tanto los datos provenientes de experiencias de triaje como la opinión de médicos expertos en urgencias. El sistema será utilizado con una doble finalidad: a nivel teórico para entender cómo la información requerida en el triaje puede ser modelada mediante redes bayesianas y a nivel práctico para entrenamiento y uso por el personal de triaje.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Bayesian estimates of linkage disequilibrium

[Background] The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. [Results] This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNP)s that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. [Conclusion] Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.Research supported by NIH/NHLBI grant R21 HL080463-01, NIH/NIDDK 1R01DK069646-01A1 and the Spanish research program [projects TIN2004-06204-C03-02 and TIN2005-02516]

A comparison of genomic profiles of complex diseases under different models

Background: Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results: We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions: In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.This work was supported by the Spanish Secretary of Research, Development and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III [PI13/02714]and [PI13/01527] and the Andalusian Research Program under project P08-TIC-03717 with the help of the European Regional Development Fund (ERDF). The authors are very grateful to the reviewers, as they believe that their comments have helped to substantially improve the quality of the paper

IL2RA/CD25 Gene Polymorphisms: Uneven Association with Multiple Sclerosis (MS) and Type 1 Diabetes (T1D)

[Background] IL-2 receptor (IL2R) alpha is the specific component of the high affinity IL2R system involved in the immune response and in the control of autoimmunity. [Methods and Results] Here we perform a replication and fine mapping of the IL2RA gene region analyzing 3 SNPs previously associated with multiple sclerosis (MS) and 5 SNPs associated with type 1 diabetes (T1D) in a collection of 798 MS patients and 927 matched Caucasian controls from the south of Spain. We observed association with MS in 6 of 8 SNPs. The rs1570538, at the 3′- UTR extreme of the gene, previously reported to have a weak association with MS, is replicated here (P = 0.032). The most associated T1D SNP (rs41295061) was not associated with MS in the present study. However, the rs35285258, belonging to another independent group of SNPs associated with T1D, showed the maximal association in this study but different risk allele. We replicated the association of only one (rs2104286) of the two IL2RA SNPs identified in the recently performed genome-wide association study of MS. [Conclusions] These findings confirm and extend the association of this gene with MS and reveal a genetic heterogeneity of the associated polymorphisms and risk alleles between MS and T1D suggesting different immunopathological roles of IL2RA in these two diseases.Financial support for the study was provided by the Ministerio de Educación y Ciencia (grants PN-SAF2006-02023 and TIN2007-67418-C03-03) and Junta de Andalucía (P07-CVI-02551) to A. Alcina and Servicio Andaluz de Salud de la Junta de Andalucía (grant PI0168/2007) to F. Matesanz. María Fedetz is a holder of a fellowship from Fundación IMABIS. Dorothy Ndagire is a holder of AECI-Ministerio de Asuntos Exteriores fellowship

Genome-wide association filtering using a highly locus-specific transmission/disequilibrium test

Multimarker transmission/disequilibrium tests (TDTs) are powerful association and linkage tests used to perform genome-wide filtering in the search for disease susceptibility loci. In contrast to case/control studies, they have a low rate of false positives for population stratification and admixture. However, the length of a region found in association with a disease is usually very large because of linkage disequilibrium (LD). Here, we define a multimarker proportional TDT (mTDTP) designed to improve locus specificity in complex diseases that has good power compared to the most powerful multimarker TDTs. The test is a simple generalization of a multimarker TDT in which haplotype frequencies are used to weight the effect that each haplotype has on the whole measure. Two concepts underlie the features of the metric: the ‘common disease, common variant’ hypothesis and the decrease in LD with chromosomal distance. Because of this decrease, the frequency of haplotypes in strong LD with common disease variants decreases with increasing distance from the disease susceptibility locus. Thus, our haplotype proportional test has higher locus specificity than common multimarker TDTs that assume a uniform distribution of haplotype probabilities. Because of the common variant hypothesis, risk haplotypes at a given locus are relatively frequent and a metric that weights partial results for each haplotype by its frequency will be as powerful as the most powerful multimarker TDTs. Simulations and real data sets demonstrate that the test has good power compared with the best tests but has remarkably higher locus specificity, so that the association rate decreases at a higher rate with distance from a disease susceptibility or disease protective locus

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research