16 research outputs found

    Relación entre las alteraciones del sueño y la enfermedad de Alzheimer: intervenciones enfermeras relacionadas con el sueño

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    Introducción: La Enfermería tiene como uno de sus fines el cuidado y atención de las personas para mantener su salud. Un sueño reparador y el descanso son necesarios para el mantenimiento de la salud. Recientes estudios sugieren una relación bidireccional entre las alteraciones del sueño y la enfermedad de Alzheimer. Objetivos: El objetivo principal es conocer la relación existente entre las alteraciones del sueño y el posterior desarrollo del Alzheimer. Metodología: Se realizó una revisión sistemática mediante una búsqueda con descriptores DeCS/MeSH y palabras clave, introduciéndolas en bases de datos y aplicando criterios específicos para conseguir una selección de artículos más precisa. Se obtuvieron 3.860 estudios tras la búsqueda y se analizaron un total de 21 publicaciones. Resultados: Tras el análisis de los estudios, 15 han estudiado la relación entre alguna alteración del sueño con el riesgo de desarrollar Alzheimer y 6 tratan sobre las intervenciones a realizar para mejorar la higiene del sueño. Se confirma que las personas con problemas de sueño tienen un mayor riesgo de padecer Alzheimer. Discusión: Hay discrepancias sobre cuáles son las alteraciones del sueño asociadas al mayor riesgo de Alzheimer. Las intervenciones eficaces para mejorar el sueño son diferentes en personas con o sin alteraciones del sueño. Aun así, se incide muy poco en establecer medidas preventivas relacionadas con el sueño para prevenir el Alzheimer. Se resalta la importancia de llevar a cabo intervenciones enfermeras relacionadas con la higiene del sueño que garanticen la salud y calidad de vida de las personas mediante la concienciación, formación y educación para la salud. Conclusiones: Las personas con alteraciones del sueño tienen un mayor riesgo de desarrollar Alzheimer, especialmente las personas con insomnio y apnea obstructiva del sueño. Se destaca la importancia de la prevención incidiendo sobre estrategias eficaces para mejorar la higiene o calidad del sueño

    Unsupervised learning methods for identifying and evaluating disease clusters in electronic health records

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    Introduction Clustering algorithms are a class of algorithms that can discover groups of observations in complex data and are often used to identify subtypes of heterogeneous diseases in electronic health records (EHR). Evaluating clustering experiments for biological and clinical significance is a vital but challenging task due to the lack of consensus on best practices. As a result, the translation of findings from clustering experiments to clinical practice is limited. Aim The aim of this thesis was to investigate and evaluate approaches that enable the evaluation of clustering experiments using EHR. Methods We conducted a scoping review of clustering studies in EHR to identify common evaluation approaches. We systematically investigated the performance of the identified approaches using a cohort of Alzheimer's Disease (AD) patients as an exemplar comparing four different clustering methods (K-means, Kernel K-means, Affinity Propagation and Latent Class Analysis.). Using the same population, we developed and evaluated a method (MCHAMMER) that tested whether clusterable structures exist in EHR. To develop this method we tested several cluster validation indexes and methods of generating null data to see which are the best at discovering clusters. In order to enable the robust benchmarking of evaluation approaches, we created a tool that generated synthetic EHR data that contain known cluster labels across a range of clustering scenarios. Results Across 67 EHR clustering studies, the most popular internal evaluation metric was comparing cluster results across multiple algorithms (30% of studies). We examined this approach conducting a clustering experiment on AD patients using a population of 10,065 AD patients and 21 demographic, symptom and comorbidity features. K-means found 5 clusters, Kernel K means found 2 clusters, Affinity propagation found 5 and latent class analysis found 6. K-means 4 was found to have the best clustering solution with the highest silhouette score (0.19) and was more predictive of outcomes. The five clusters found were: typical AD (n=2026), non-typical AD (n=1640), cardiovascular disease cluster (n=686), a cancer cluster (n=1710) and a cluster of mental health issues, smoking and early disease onset (n=1528), which has been found in previous research as well as in the results of other clustering methods. We created a synthetic data generation tool which allows for the generation of realistic EHR clusters that can vary in separation and number of noise variables to alter the difficulty of the clustering problem. We found that decreasing cluster separation did increase cluster difficulty significantly whereas noise variables increased cluster difficulty but not significantly. To develop the tool to assess clusters existence we tested different methods of null dataset generation and cluster validation indices, the best performing null dataset method was the min max method and the best performing indices we Calinksi Harabasz index which had an accuracy of 94%, Davies Bouldin index (97%) silhouette score ( 93%) and BWC index (90%). We further found that when clusters were identified using the Calinski Harabasz index they were more likely to have significantly different outcomes between clusters. Lastly we repeated the initial clustering experiment, comparing 10 different pre-processing methods. The three best performing methods were RBF kernel (2 clusters), MCA (4 clusters) and MCA and PCA (6 clusters). The MCA approach gave the best results highest silhouette score (0.23) and meaningful clusters, producing 4 clusters; heart and circulatory( n=1379), early onset mental health (n=1761), male cluster with memory loss (n = 1823), female with more problem (n=2244). Conclusion We have developed and tested a series of methods and tools to enable the evaluation of EHR clustering experiments. We developed and proposed a novel cluster evaluation metric and provided a tool for benchmarking evaluation approaches in synthetic but realistic EHR

    Generative models of brain connectivity for population studies

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-139).Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may enhance our understanding of neurological disorders. This thesis proposes a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations. Our formulation captures the interaction between hidden templates of anatomical and functional connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns predictive differences between a control and a schizophrenia population. Furthermore, combining the two modalities yields better results than considering each one in isolation. Although our joint model identifies widespread connectivity patterns influenced by a neurological disorder, the results are difficult to interpret and integrate with our regioncentric knowledge of the brain. To alleviate this problem, we present a novel approach to identify regions associated with the disorder based on connectivity information. Specifically, we assume that impairments of the disorder localize to a small subset of brain regions, which we call disease foci, and affect neural communication to/from these regions. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. Our inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant functional connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.by Archana Venkataraman.Ph.D

    Strukturální podklady kognitivního deficitu v zobrazování magnetické rezonance.

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    Předkládaná dizertační práce se ve své hlavní části zabývá možnostmi detekce strukturálních a difuzních změn v MR zobrazení u pacientů s kognitivním deficitem. V širším kontextu je nejprve zmíněn podklad klinických změn a nálezů při neurozobrazení u pacientů s demencí, a to se zvláštním zaměřením na Alzheimerovu chorobu (ACh) a její diferenciální diagnostiku. Druhá část práce obsahuje čtyři experimentální studie v rámci našeho výzkumu. Hlavním cílem prvních dvou studií bylo získání strukturální a mikrostrukturální informace o neurodegenerativních procesech charakteristických pro ACh - na globální i regionální úrovni. Pro tento účel bylo použito několik komplementárních přístupů se zaměřením především na evaluaci šedé, a následně i bílé hmoty mozku. V následujících částech jsme se zaměřili na popis kontextu mikrostrukturálních změn bílé hmoty u normotenzního hydrocefalu (NPH) a charakteristických vzorců dezintegrace bílé hmoty u epilepsií temporálního laloku (TLE). Nejdůležitějším závěrem, který lze vyvodit z našich studií je, že strukturální a difuzní zobrazování se ukázalo jako užitečné při identifikaci regionálně specifické a disproporcionální ztráty objemu mozku a mikrostruktury u některých patologických procesů, které jsou základem kognitivního zhoršení. Použití několika různých morfometrických...Structural and diffusion imaging patterns that can be evaluated using MRI in patients with cognitive deficits are the central theme of the proposed work. First, the clinical and neuroimaging background of dementias has been reviewed in a broader context, with a special focus on Alzheimer's disease (AD) and differential diagnoses. The second part of this thesis contains four consecutive experimental studies. The primary objective of the first two studies was to obtain structural and microstructural information on the neurodegenerative processes characteristic for AD on global and regional levels. For this purpose, several complementary approaches were used and the focus was shifted from grey to white matter (GM/WM). The following two studies focused on the differential context of WM microstructural alterations in normal pressure hydrocephalus (NPH) and distinctive patterns of WM disintegrity in temporal lobe epilepsy (TLE). The most important conclusion of our studies is that structural and diffusion imaging proved to be useful in identifying regionally specific and disproportionate loss of brain volume and microstructure in several pathological processes underlying cognitive deterioration. The use of distinctive morphometric methods yielded complementary information on AD-related atrophy patterns,...Department of Neurosurgery and Neurooncology First Faculty of Medicine and Central Military HospitalNeurochirurgická a neuroonkologická klinika 1. LF UK a ÚVN1. lékařská fakultaFirst Faculty of Medicin

    Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

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    Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease

    Pacific Symposium on Biocomputing 2023

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    The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

    Structural building blocks in graph data : characterised by hyperbolic communities and uncovered by Boolean tensor clustering

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    Graph data nowadays easily become so large that it is infeasible to study the underlying structures manually. Thus, computational methods are needed to uncover large-scale structural information. In this thesis, we present methods to understand and summarise large networks. We propose the hyperbolic community model to describe groups of more densely connected nodes within networks using very intuitive parameters. The model accounts for a frequent connectivity pattern in real data: a few community members are highly interconnected; most members mainly have ties to this core. Our model fits real data much better than previously-proposed models. Our corresponding random graph generator, HyGen, creates graphs with realistic intra-community structure. Using the hyperbolic model, we conduct a large-scale study of the temporal evolution of communities on online question–answer sites. We observe that the user activity within a community is constant with respect to its size throughout its lifetime, and a small group of users is responsible for the majority of the social interactions. We propose an approach for Boolean tensor clustering. This special tensor factorisation is restricted to binary data and assumes that one of the tensor directions has only non-overlapping factors. These assumptions – valid for many real-world data, in particular time-evolving networks – enable the use of bitwise operators and lift much of the computational complexity from the task.Netzwerke sind heutzutage oft so groß und unübersichtlich, dass manuelle Analysen nicht reichen, um sie zu verstehen. Um zugrundeliegende Strukturen im großen Maßstab zu identifizieren, bedarf es computergestützter Methoden. Unser Modell für hyperbolische Gemeinschaften beschreibt die innere Struktur eng verknüpfter Knotengruppen in Netzwerken mit sehr eingängigen Parametern. Es basiert auf der Beobachtung, dass oft ein kleiner Teil der Knoten einer Gruppe eng miteinander verknüpft ist und die Mehrheit der Gruppenmitglieder nur Verbindungen zu diesem Zentrum aufweist. Unser Modell bildet echte Daten besser ab als bisherige Modelle. Der entsprechende Zufallsgraphgenerator, HyGen, erzeugt Graphen mit realistischen innergemeinschaftlichen Strukturen. Anhand unseres Modells analysieren wir die Bildung von Gemeinschaften in online Frage-und-Antwort-Netzwerken. Wir beobachten, dass die Aktivität der Mitglieder über die Zeit konstant ist, bezogen auf die Größe der jeweiligen Gemeinschaft. Außerdem ist stets eine kleine Gruppe von Mitgliedern verantwortlich für den Großteil der Aktivität. Wir schlagen eine Methode für Boolesches Tensor Clustering vor. Diese spezielle Tensorfaktorisierung ist beschränkt auf binäre Daten und wir nehmen an, dass es entlang einer Richtung des Tensors keinen nennenswerten Überlapp der Faktoren gibt. Diese Annahmen ermöglichen die Nutzung von Bitoperationen, mindern den Rechenaufwand erheblich und passen gut zu dem, was in echten Daten zu beobachten ist.Max-Planck-Institut für Informati
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