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

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

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

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

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]

Revisión bibliográfica de implantología bucofacial del año 2007

Se expone una revisión de la literatura científica publicada en revistas indexadas durante el año 2007 sobre Implantología Bucofacial. La escasez de tiempo de que disponen los profesionales para consultar las múltiples fuentes de información, ha motivado a los autores a resumir los artículos publicados y clasificarlos en los siguientes apartados: generalidades, pacientes especiales, superficies y diseños, tejidos blandos, implantes inmediatos, carga inmediata, complicaciones, elevación sinusal, técnicas avanzadas, plasma rico en plaquetas y factores de crecimiento, cirugía guiada, cirugía mínimamente invasiva y miniimplantes, con la intención de facilitar una puesta al día

Sample Reproducibility of Genetic Association Using Different Multimarker TDTs in Genome-Wide Association Studies: Characterization and a New Approach

Multimarker Transmission/Disequilibrium Tests (TDTs) are very robust association tests to population admixture and structure which may be used to identify susceptibility loci in genome-wide association studies. Multimarker TDTs using several markers may increase power by capturing high-degree associations. However, there is also a risk of spurious associations and power reduction due to the increase in degrees of freedom. In this study we show that associations found by tests built on simple null hypotheses are highly reproducible in a second independent data set regardless the number of markers. As a test exhibiting this feature to its maximum, we introduce the multimarker -Groups TDT (), a test which under the hypothesis of no linkage, asymptotically follows a distribution with degree of freedom regardless the number of markers. The statistic requires the division of parental haplotypes into two groups: disease susceptibility and disease protective haplotype groups. We assessed the test behavior by performing an extensive simulation study as well as a real-data study using several data sets of two complex diseases. We show that test is highly efficient and it achieves the highest power among all the tests used, even when the null hypothesis is tested in a second independent data set. Therefore, turns out to be a very promising multimarker TDT to perform genome-wide searches for disease susceptibility loci that may be used as a preprocessing step in the construction of more accurate genetic models to predict individual susceptibility to complex diseases

Pre-exposure prophylaxis with hydroxychloroquine for COVID-19 : a double-blind, placebo-controlled randomized clinical trial

Background: Pre-exposure prophylaxis (PrEP) is a promising strategy to break COVID-19 transmission. Although hydroxychloroquine was evaluated for treatment and post-exposure prophylaxis, it is not evaluated for COVID-19 PrEP yet. The aim of this study was to evaluate the efficacy and safety of PrEP with hydroxychloroquine against placebo in healthcare workers at high risk of SARS-CoV-2 infection during an epidemic period. Methods: We conducted a double-blind placebo-controlled randomized clinical trial in three hospitals in Barcelona, Spain. From 350 adult healthcare workers screened, we included 269 participants with no active or past SARS-CoV-2 infection (determined by a negative nasopharyngeal SARS-CoV-2 PCR and a negative serology against SARS-CoV-2). Participants allocated in the intervention arm (PrEP) received 400 mg of hydroxychloroquine daily for the first four consecutive days and subsequently, 400 mg weekly during the study period. Participants in the control group followed the same treatment schedule with placebo tablets. Results: 52.8% (142/269) of participants were in the hydroxychloroquine arm and 47.2% (127/269) in the placebo arm. Given the national epidemic incidence decay, only one participant in each group was diagnosed with COVID-19. The trial was stopped due to futility and our study design was deemed underpowered to evaluate any benefit regarding PrEP efficacy. Both groups showed a similar proportion of participants experiencing at least one adverse event (AE) (p=0.548). No serious AEs were reported. Almost all AEs (96.4%, 106/110) were mild. Only mild gastrointestinal symptoms were significantly higher in the hydroxychloroquine arm compared to the placebo arm (27.4% (39/142) vs 15.7% (20/127), p=0.041). Conclusions: Although the efficacy of PrEP with hydroxychloroquine for preventing COVID-19 could not be evaluated, our study showed that PrEP with hydroxychloroquine at low doses is safe. Trial registration: ClinicalTrials.govNCT04331834. Registered on April 2, 2020