Bibliographies thématiques / Heterogeneous health data

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Littérature scientifique sur le sujet « Heterogeneous health data »

Auteur : Grafiati
Publié le 25 janvier 2025

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Articles de revues sur le sujet "Heterogeneous health data"

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Li, Gang-Guo, et Zheng-Zhi Wang. « Incorporating heterogeneous biological data sources in clustering gene expression data ». Health 01, no 01 (2009) : 17–23. http://dx.doi.org/10.4236/health.2009.11004.

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Mohammed, Noman, Xiaoqian Jiang, Rui Chen, Benjamin C. M. Fung et Lucila Ohno-Machado. « Privacy-preserving heterogeneous health data sharing ». Journal of the American Medical Informatics Association 20, no 3 (13 décembre 2012) : 462–69. http://dx.doi.org/10.1136/amiajnl-2012-001027.

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Hadzi-Pavlovic, Dusan. « Correlations III : heterogeneous data ». Acta Neuropsychiatrica 19, no 3 (juin 2007) : 215–16. http://dx.doi.org/10.1111/j.1601-5215.2007.00219.x.

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Sunindyo, Wikan Danar, Thomas Moser, Dietmar Winkler et Stefan Biffl. « Analyzing OSS Project Health with Heterogeneous Data Sources ». International Journal of Open Source Software and Processes 3, no 4 (octobre 2011) : 1–23. http://dx.doi.org/10.4018/jossp.2011100101.

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Stakeholders in Open Source Software (OSS) projects need to determine whether a project is likely to sustain for a sufficient period of time in order to justify their investments into this project. In an OSS project context, there are typically several data sources and OSS processes relevant for determining project health indicators. However, even within one project these data sources often are technically and/or semantically heterogeneous, which makes data collection and analysis tedious and error prone. In this paper, the authors propose and evaluate a framework for OSS data analysis (FOSSDA), which enables the efficient collection, integration, and analysis of data from heterogeneous sources. Major results of the empirical studies are: (a) the framework is useful for integrating data from heterogeneous data sources effectively and (b) project health indicators based on integrated data analyses were found to be more accurate than analyses based on individual non-integrated data sources.
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Zhao, Jing, Panagiotis Papapetrou, Lars Asker et Henrik Boström. « Learning from heterogeneous temporal data in electronic health records ». Journal of Biomedical Informatics 65 (janvier 2017) : 105–19. http://dx.doi.org/10.1016/j.jbi.2016.11.006.

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Ganguly, Sukanta, Pavandeep Kataria, Radmila Juric, Atila Ertas et Murat M. Tanik. « Sharing Information and Data Across Heterogeneous e-Health Systems ». Telemedicine and e-Health 15, no 5 (juin 2009) : 454–64. http://dx.doi.org/10.1089/tmj.2008.0149.

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Monga, H. K., et T. B. Patrick. « Error estimation in linking heterogeneous data sources ». Health Informatics Journal 7, no 3-4 (septembre 2001) : 135–37. http://dx.doi.org/10.1177/146045820100700305.

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Bleischwitz, Sinja, Tristan Salomon Winkelmann, Yvonne Pfeifer, Martin Alexander Fischer, Niels Pfennigwerth, Jens André Hammerl, Ulrike Binsker et al. « Antimicrobial Resistance Surveillance : Data Harmonisation and Data Selection within Secondary Data Use ». Antibiotics 13, no 7 (16 juillet 2024) : 656. http://dx.doi.org/10.3390/antibiotics13070656.

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Resistance to last-resort antibiotics is a global threat to public health. Therefore, surveillance and monitoring systems for antimicrobial resistance should be established on a national and international scale. For the development of a One Health surveillance system, we collected exemplary data on carbapenem and colistin-resistant bacterial isolates from human, animal, food, and environmental sources. We pooled secondary data from routine screenings, hospital outbreak investigations, and studies on antimicrobial resistance. For a joint One Health evaluation, this study incorporates epidemiological metadata with phenotypic resistance information and molecular data on the isolate level. To harmonise the heterogeneous original information for the intended use, we developed a generic strategy. By defining and categorising variables, followed by plausibility checks, we created a catalogue for prospective data collections and applied it to our dataset, enabling us to perform preliminary descriptive statistical analyses. This study shows the complexity of data management using heterogeneous secondary data pools and gives an insight into the early stages of the development of an AMR surveillance programme using secondary data.
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Paragliola, Giovanni, et Patrizia Ribino. « Exploring heterogeneous data distribution issues in e-health federated systems ». Biomedical Signal Processing and Control 92 (juin 2024) : 106039. http://dx.doi.org/10.1016/j.bspc.2024.106039.

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Li, Ruohong, Honglang Wang et Wanzhu Tu. « Robust estimation of heterogeneous treatment effects using electronic health record data ». Statistics in Medicine 40, no 11 (19 mars 2021) : 2713–52. http://dx.doi.org/10.1002/sim.8926.

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Thèses sur le sujet "Heterogeneous health data"

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Nugawela, Saliya. « Data warehousing model for integrating fragmented electronic health records from disparate and heterogeneous clinical data stores ». Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/60880/1/Saliya_Nugawela_Thesis.pdf.

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This research was a step forward in developing a data integration framework for Electronic Health Records. The outcome of the research is a conceptual and logical Data Warehousing model for integrating Cardiac Surgery electronic data records. This thesis investigated the main obstacles for the healthcare data integration and proposes a data warehousing model suitable for integrating fragmented data in a Cardiac Surgery Unit.
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Wootton, Adam J. « Fusion of heterogeneous data in non-destructive testing and structural health monitoring using Echo State Networks ». Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5004/.

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Failure to monitor the condition of key infrastructure such as roads and bridges can result in costly closures, but the economic impact could be lessened by early intervention. Non-destructive testing (NDT) examines structures without causing damage, while structural health monitoring (SHM) monitors a structure throughout its life. This thesis presents a machine learning approach to fusing heterogeneous sensor modalities that can be systematically applied to improve sensor interpretation and reduce reliance on expertise. For the first time, echo state networks (ESNs) were used in two separate NDT and SHM data fusion case studies. The NDT-based study looked at detecting defects in steel reinforcement, teaching ESNs to combine magnetic flux leakage (MFL) and cover depth data in order to compensate for variation in MFL amplitude with increasing cover depth. Using seven different cover depths between 42.5 mm and 289 mm, the fusion approach offered improved performance for 42.5mm < depth < 205mm and the most consistent calculated optimal output threshold, demonstrating the ease of systematic application. In the SHM-based study, data from the National Physical Laboratory (NPL) footbridge monitoring project was processed by a suite of ESNs to detect, localise, classify and assess damage caused by deliberate interventions. A novel approach of combining physical and environmental sensors in order to model a different modality of physical sensor made it possible to use the residual to observe damage trends and locations, which also led to the isolation of a faulty strain gauge. There was additional success in distinguishing between different intervention types and producing a metric to express the damage level. Across both studies, the ESN approach to heterogeneous data fusion improved upon non-fusion-based alternatives. This suggests that future work should consider structures that are in regular use, combining further sensor modalities and the development of bespoke data fusion software.
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Griffier, Romain. « Intégration et utilisation secondaire des données de santé hospitalières hétérogènes : des usages locaux à l'analyse fédérée ». Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0479.

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Les données issues du soin peuvent être utilisées pour des finalités autres que celles pour lesquelles elles ont été collectées initialement : c’est l’utilisation secondaire des données de santé. Dans le contexte hospitalier, afin de lever les verrous de l’utilisation secondaire des données de santé (verrous liés aux données et verrous organisationnels), une stratégie classique consiste à mettre en place un Entrepôt de Données de Santé (EDS). Dans le cadre de cette thèse, trois contributions à l’EDS du CHU de Bordeaux sont décrites. Premièrement, une méthode d’alignement des data éléments de biologie numérique basée sur les instances et conforme aux règles de protection des données à caractère personnel est présentée, avec une F-mesure à 0,850, permettant de réduire l’hétérogénéité sémantique des données. Ensuite, une adaptation du modèle d’intégration des données cliniques d’i2b2 est proposée pour assurer la persistance des données d’un EDS dans une base de données NoSQL, Elasticsearch. Cette implémentation a été évaluée sur la base de données de l’EDS du CHU de Bordeaux et retrouve des performances améliorées en termes de stockage et de temps de requêtage, par rapport à une base de données relationnelle. Enfin, une présentation de l’environnement EDS du CHU de Bordeaux est réalisée, avec la description d’un premier EDS dédié aux usages locaux et qui peut être exploité en autonomie par les utilisateurs finaux (i2b2), et d’un second EDS, dédié aux réseaux fédérés (OMOP) permettant notamment la participation au réseau fédéré DARWIN-EU
Healthcare data can be used for purposes other than those for which it was initially collected: this is the secondary use of health data. In the hospital context, to overcome the obstacles to secondary use of healthcaree data (data and organizational barriers), a classic strategy is to set up Clinical Data Warehouses (CDWs). This thesis describes three contributions to the Bordeaux University Hospital’s CDW. Firstly, an instance-based, privacy-preserving, method for mapping numerical biology data elements is presented, with an F-measure of 0,850, making it possible to reduce the semantic heterogeneity of data. Next, an adaptation of the i2b2 clinical data integration model is proposed to enable CDW data persistence in a NoSQL database, Elasticsearch. This implementation has been evaluated on the Bordeaux University Hospital’s CDW, showing improved performance in terms of storage and query time, compared with a relational database. Finally, the Bordeaux University Hospital’s CDW environment is presented, with the description of a first CDW dedicated to local uses that can be used autonomously by end users (i2b2), and a second CDW dedicated to federated networks (OMOP) enabling participation in the DARWIN-EU federated network
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McInerney, Sean. « Parameterising continuum models of heat transfer in heterogeneous living skin using experimental data ». Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/123772/1/Sean_McInerney_Thesis.pdf.

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This research project looked to describe the heat transfer process associated with a burn injury using a mathematical model. The ability to theoretically infer the unknown parameters of the model is investigated through the consideration of different experimental designs. The results of this thesis can inform future experimentalists on how to maximise the information gained from their work.
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Darbon, Alexandre. « Épidémiologie sur réseau pour l'évaluation des risques dans la prévention et le contrôle des infections Network-based assessment of the vulnerability of Italian regions to bovine brucellosis Disease persistence on temporal contact networks accounting for heterogeneous infectious periods ». Thesis, Sorbonne université, 2019. http://www.theses.fr/2019SORUS077.

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L’objectif de ma thèse est de proposer des solutions contre la propagation des maladies infectieuses dans des cas précis, en tenant compte de l'évolution des contacts entre les hôtes. Ce travail porte en particulier sur la détermination du seuil épidémique, un indicateur clé du risque épidémique. Il exploite et étend un formalisme mathématique issu de la théorie des réseaux, qui permet de déterminer le seuil épidémique dans des situations réelles, pour en dégager des mesures de santé publique. Un premier projet met en lumière des facteurs à l'origine de la persistance de la brucellose bovine en Italie en dépit des mesures d'éradication en place. L'approche théorique permet de calculer le seuil épidémique dans chaque région du pays à l'aide de données exhaustives sur les déplacements de bovins entre les exploitations italiennes sur plusieurs années, ainsi que des relevés datés de flambées épidémiques. Est ensuite présentée une extension du formalisme qui prend en compte différentes durées moyennes d’infection dans le calcul du seuil épidémique. Ce travail montre dans différents contextes épidémiologiques comment l’hypothèse classique selon laquelle la durée moyenne d’infection est homogène peut biaiser l’estimation du risque épidémique. Cette méthode permet également d'identifier les hôtes d'une population qui sont principalement responsables du risque épidémique global
My doctoral thesis aims to propose solutions against the spread of infectious diseases in specific contexts, taking into account how host contacts evolve in time using a temporal network representation. It focuses on the determination of the epidemic threshold, a key indicator of the epidemic risk. By leveraging and extending a mathematical formalism from network theory, this work enables the computation of the epidemic threshold in real situations in order to identify public health measures. A first project addresses the persistence of bovine brucellosis in Italy despite the existing eradication measures. Using comprehensive data on cattle movements between Italian farms over several years, as well as time-stamped outbreak records, the epidemic threshold computation in each region of the country provides information on regions vulnerability and proposes factors that may explain disease persistence. An extension of the formalism is then presented, including heterogeneous average infectious periods in the epidemic threshold computation. This work shows in different epidemiological contexts how the classical assumption that the average infectious period is the same for all hosts in a population may bias epidemic risk assessments. This method also identifies the hosts in a population that are primarily responsible for the global epidemic risk
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Ayvazyan, Vigen. « Etude de champs de température séparables avec une double décomposition en valeurs singulières : quelques applications à la caractérisation des propriétés thermophysiques des matérieux et au contrôle non destructif ». Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14671/document.

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La thermographie infrarouge est une méthode largement employée pour la caractérisation des propriétés thermophysiques des matériaux. L’avènement des diodes laser pratiques, peu onéreuses et aux multiples caractéristiques, étendent les possibilités métrologiques des caméras infrarouges et mettent à disposition un ensemble de nouveaux outils puissants pour la caractérisation thermique et le contrôle non desturctif. Cependant, un lot de nouvelles difficultés doit être surmonté, comme le traitement d’une grande quantité de données bruitées et la faible sensibilité de ces données aux paramètres recherchés. Cela oblige de revisiter les méthodes de traitement du signal existantes, d’adopter de nouveaux outils mathématiques sophistiqués pour la compression de données et le traitement d’informations pertinentes. Les nouvelles stratégies consistent à utiliser des transformations orthogonales du signal comme outils de compression préalable de données, de réduction et maîtrise du bruit de mesure. L’analyse de sensibilité, basée sur l’étude locale des corrélations entre les dérivées partielles du signal expérimental, complète ces nouvelles approches. L'analogie avec la théorie dans l'espace de Fourier a permis d'apporter de nouveaux éléments de réponse pour mieux cerner la «physique» des approches modales.La réponse au point source impulsionnel a été revisitée de manière numérique et expérimentale. En utilisant la séparabilité des champs de température nous avons proposé une nouvelle méthode d'inversion basée sur une double décomposition en valeurs singulières du signal expérimental. Cette méthode par rapport aux précédentes, permet de tenir compte de la diffusion bi ou tridimensionnelle et offre ainsi une meilleure exploitation du contenu spatial des images infrarouges. Des exemples numériques et expérimentaux nous ont permis de valider dans une première approche cette nouvelle méthode d'estimation pour la caractérisation de diffusivités thermiques longitudinales. Des applications dans le domaine du contrôle non destructif des matériaux sont également proposées. Une ancienne problématique qui consiste à retrouver les champs de température initiaux à partir de données bruitées a été abordée sous un nouveau jour. La nécessité de connaitre les diffusivités thermiques du matériau orthotrope et la prise en compte des transferts souvent tridimensionnels sont complexes à gérer. L'application de la double décomposition en valeurs singulières a permis d'obtenir des résultats intéressants compte tenu de la simplicité de la méthode. En effet, les méthodes modales sont basées sur des approches statistiques de traitement d'une grande quantité de données, censément plus robustes quant au bruit de mesure, comme cela a pu être observé
Infrared thermography is a widely used method for characterization of thermophysical properties of materials. The advent of the laser diodes, which are handy, inexpensive, with a broad spectrum of characteristics, extend metrological possibilities of infrared cameras and provide a combination of new powerful tools for thermal characterization and non destructive evaluation. However, this new dynamic has also brought numerous difficulties that must be overcome, such as high volume noisy data processing and low sensitivity to estimated parameters of such data. This requires revisiting the existing methods of signal processing, adopting new sophisticated mathematical tools for data compression and processing of relevant information.New strategies consist in using orthogonal transforms of the signal as a prior data compression tools, which allow noise reduction and control over it. Correlation analysis, based on the local cerrelation study between partial derivatives of the experimental signal, completes these new strategies. A theoretical analogy in Fourier space has been performed in order to better understand the «physical» meaning of modal approaches.The response to the instantaneous point source of heat, has been revisited both numerically and experimentally. By using separable temperature fields, a new inversion technique based on a double singular value decomposition of experimental signal has been introduced. In comparison with previous methods, it takes into account two or three-dimensional heat diffusion and therefore offers a better exploitation of the spatial content of infrared images. Numerical and experimental examples have allowed us to validate in the first approach our new estimation method of longitudinal thermal diffusivities. Non destructive testing applications based on the new technique have also been introduced.An old issue, which consists in determining the initial temperature field from noisy data, has been approached in a new light. The necessity to know the thermal diffusivities of an orthotropic medium and the need to take into account often three-dimensional heat transfer, are complicated issues. The implementation of the double singular value decomposition allowed us to achieve interesting results according to its ease of use. Indeed, modal approaches are statistical methods based on high volume data processing, supposedly robust as to the measurement noise
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Pivovarov, Rimma. « Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data ». Thesis, 2015. https://doi.org/10.7916/D89W0F6V.

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The increasing adoption of electronic health records (EHRs) has led to an unprecedented amount of patient health information stored in an electronic format. The ability to comb through this information is imperative, both for patient care and computational modeling. Creating a system to minimize unnecessary EHR data, automatically distill longitudinal patient information, and highlight salient parts of a patient’s record is currently an unmet need. However, summarization of EHR data is not a trivial task, as there exist many challenges with reasoning over this data. EHR data elements are most often obtained at irregular intervals as patients are more likely to receive medical care when they are ill, than when they are healthy. The presence of narrative documentation adds another layer of complexity as the notes are riddled with over-sampled text, often caused by the frequent copy-and-pasting during the documentation process. This dissertation synthesizes a set of challenges for automated EHR summarization identified in the literature and presents an array of methods for dealing with some of these challenges. We used hybrid data-driven and knowledge-based approaches to examine abundant redundancy in clinical narrative text, a data-driven approach to identify and mitigate biases in laboratory testing patterns with implications for using clinical data for research, and a probabilistic modeling approach to automatically summarize patient records and learn computational models of disease with heterogeneous data types. The dissertation also demonstrates two applications of the developed methods to important clinical questions: the questions of laboratory test overutilization and cohort selection from EHR data.
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« Novel Statistical Learning Methods for Multi-Modality Heterogeneous Data Fusion in Health Care Applications ». Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.53553.

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abstract: With the development of computer and sensing technology, rich datasets have become available in many fields such as health care, manufacturing, transportation, just to name a few. Also, data come from multiple heterogeneous sources or modalities. This is a common phenomenon in health care systems. While multi-modality data fusion is a promising research area, there are several special challenges in health care applications. (1) The integration of biological and statistical model is a big challenge; (2) It is commonplace that data from various modalities is not available for every patient due to cost, accessibility, and other reasons. This results in a special missing data structure in which different modalities may be missed in “blocks”. Therefore, how to train a predictive model using such a dataset poses a significant challenge to statistical learning. (3) It is well known that different modality data may contain different aspects of information about the response. The current studies cannot afford to solve this problem. My dissertation includes new statistical learning model development to address each of the aforementioned challenges as well as application case studies using real health care datasets, included in three chapters (Chapter 2, 3, and 4), respectively. Collectively, it is expected that my dissertation could provide a new sets of statistical learning models, algorithms, and theory contributed to multi-modality heterogeneous data fusion driven by the unique challenges in this area. Also, application of these new methods to important medical problems using real-world datasets is expected to provide solutions to these problems, and therefore contributing to the application domains.
Dissertation/Thesis
Doctoral Dissertation Industrial Engineering 2019
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Hoklas, Megan Marie. « An integrated latent construct modeling framework for predicting physical activity engagement and health outcomes ». Thesis, 2014. http://hdl.handle.net/2152/28257.

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The health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns.
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Livres sur le sujet "Heterogeneous health data"

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Pivovarov, Rimma. Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data. [New York, N.Y.?] : [publisher not identified], 2015.

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Nilipour, Reza. Neurolinguistics. Sous la direction de Anousha Sedighi et Pouneh Shabani-Jadidi. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198736745.013.18.

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This chapter summarizes some first neurolinguistic studies conducted in Persian, using patholinguistic data taken from monolingual and bilingual brain-damaged patients, as well as five first neuroimaging studies in healthy native speakers of Persian. The patholinguistic data are extracted from formal clinical linguistic assessments of a heterogeneous group of brain-damaged patients with different etiologies and brain lesion sites. The data are indicative of general agrammatic features of ‘syntactic simplification’ and ‘morphological regression’ reported in cross-language studies, along with language-particular agrammatic features in spoken and written modalities for Persian consequent to brain damage. The present patholinguistic data are also suggestive of a ‘non-unitary’ model of aphasia as a symptom complex phenomenon with disruptions of independent linguistic levels consequent to different lesion sites. The data are not supportive of independent production and comprehension language centres claimed in ‘classical model’ of brain and language but in support of new non-narrow localization brain–language models.
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Chapitres de livres sur le sujet "Heterogeneous health data"

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Ali, Rafat, et Nida Jamil Khan. « Networks Analytics of Heterogeneous Big Data ». Dans Biological Networks in Human Health and Disease, 65–74. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4242-8_4.

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Savoska, Snezana, Blagoj Ristevski et Vladimir Trajkovik. « Personal Health Record Data-Driven Integration of Heterogeneous Data ». Dans Data-Intensive Research, 1–21. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5184-8_1.

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Ren, Peng, Weihang Lin, Ye Liang, Ruoyu Wang, Xingyue Liu, Baifu Zuo, Tan Chen, Xin Li, Ming Sheng et Yong Zhang. « HMDFF : A Heterogeneous Medical Data Fusion Framework Supporting Multimodal Query ». Dans Health Information Science, 254–66. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90885-0_23.

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Xiao, Qi, Wenkui Zheng, Chenyu Mao, Wei Hou, Hao Lan, Daojun Han, Yang Duan, Peng Ren et Ming Sheng. « MHDML : Construction of a Medical Lakehouse for Multi-source Heterogeneous Data ». Dans Health Information Science, 127–35. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20627-6_12.

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Cui, Qin, Wenkui Zheng, Wei Hou, Ming Sheng, Peng Ren, Wang Chang et XiangYang Li. « HoloCleanX : A Multi-source Heterogeneous Data Cleaning Solution Based on Lakehouse ». Dans Health Information Science, 165–76. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20627-6_16.

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Asadzadehzanjani, Negin, et Janusz Wojtusiak. « Administrative Health Data Representation for Mortality and High Utilization Prediction ». Dans Heterogeneous Data Management, Polystores, and Analytics for Healthcare, 133–50. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93663-1_11.

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Kale, Geetanjali, et Kalyani Waghmare. « Heterogeneous data management in IoT-based health care systems ». Dans Artificial Intelligence for Internet of Things, 49–64. Boca Raton : CRC Press, 2022. http://dx.doi.org/10.1201/9781003335801-4.

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Dubovitskaya, Alevtina, Petr Novotny, Scott Thiebes, Ali Sunyaev, Michael Schumacher, Zhigang Xu et Fusheng Wang. « Intelligent Health Care Data Management Using Blockchain : Current Limitation and Future Research Agenda ». Dans Heterogeneous Data Management, Polystores, and Analytics for Healthcare, 277–88. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33752-0_20.

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Gardner, P. A., L. A. Bull, N. Dervilis et K. Worden. « On the Application of Heterogeneous Transfer Learning to Population-Based Structural Health Monitoring ». Dans Data Science in Engineering, Volume 9, 87–98. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76004-5_11.

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Gardner, P. A., L. A. Bull, N. Dervilis et K. Worden. « On the Application of Heterogeneous Transfer Learning to Population-Based Structural Health Monitoring ». Dans Data Science in Engineering, Volume 9, 87–98. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76004-5_11.

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Actes de conférences sur le sujet "Heterogeneous health data"

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Hu, Hengrui, Anai N. Kothari et Anjishnu Banerjee. « Personalized Federated Learning for Gastric Cancer : Adaptive Inference from Large Heterogeneous Piecewise Electronic Health Record Based Biomedical Data ». Dans 2024 IEEE International Conference on Big Data (BigData), 4554–63. IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825882.

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Zheng, Yuhui, Yankun Zhang, Weiqiang Lin et Qingfeng Wu. « How Can We Design a Standardized and Efficient Health Data Management System for Large-Scale Heterogeneous TCM Data ? » Dans 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 4848–53. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822299.

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Zhao, Haotian, Bin Wang, Qinglai Guo, Yixun Xue, Yaqi Sun et Hongbin Sun. « Hybrid Model-and-Data-Driven Combined Heat and Power Economic Dispatch Based on Heterogeneous Decomposition ». Dans 2024 IEEE Power & Energy Society General Meeting (PESGM), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10689144.

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Liu, Zheng, Xiaohan Li, Hao Peng, Lifang He et Philip S. Yu. « Heterogeneous Similarity Graph Neural Network on Electronic Health Records ». Dans 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377795.

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Kiourtis, Athanasios, Argyro Mavrogiorgou et Dimosthenis Kyriazis. « Aggregating Heterogeneous Health Data through an Ontological Common Health Language ». Dans 2017 10th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2017. http://dx.doi.org/10.1109/dese.2017.9.

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SUN, HAO, et ORAL BUYUKOZTURK. « Heterogeneous Data Fusion for Traffic-induced Excitation Identification of Truss Bridges ». Dans Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/47.

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Fan, Kai, Marisa Eisenberg, Alison Walsh, Allison Aiello et Katherine Heller. « Hierarchical Graph-Coupled HMMs for Heterogeneous Personalized Health Data ». Dans KDD '15 : The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2783258.2783326.

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Lu, Xuchen, Hongling Tang, Wenli Cheng et Tingting Zhang. « Heterogeneous Data Source Middleware for Android E-Health Application ». Dans 2012 Eighth International Conference on Mobile Ad-hoc and Sensor Networks (MSN). IEEE, 2012. http://dx.doi.org/10.1109/msn.2012.16.

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Tomoda, Kyosuke, Kai Morino, Hiroshi Murata, Ryo Asaoka et Kenji Yamanishi. « Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data ». Dans 9th International Conference on Health Informatics. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005703900930104.

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Dao, Minh-Son, et Koji Zettsu. « Discovering Environmental Impacts on Public Health Using Heterogeneous Big Sensory Data ». Dans 2015 IEEE International Congress on Big Data (BigData Congress). IEEE, 2015. http://dx.doi.org/10.1109/bigdatacongress.2015.122.

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Rapports d'organisations sur le sujet "Heterogeneous health data"

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Cerda, Maikol, David Cervantes, Paul Gertler, Sean Higgins, Ana María Montoya, Eric Parrado, Carlos Serrano, Raimundo Undurraga et Patricia Yáñez-Pagans. Covid-19 Pandemic and SMEs' Performance in Latin America. Inter-American Development Bank, février 2023. http://dx.doi.org/10.18235/0004720.

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The COVID-19 pandemic generated a large negative external shock to the global economy. Businesses worldwide were affected by economic, health, and mobility restrictions that impacted consumers ability to access goods and services and firms profitability and survival rates. In this paper, we study the economic performance of Latin American MSMEs during the pandemic using disaggregated and high-frequency administrative banking deposits and income data from Brazil, Chile, Colombia, Mexico, Paraguay, and Peru. We observe a sharp short-term decline in firm earnings due to the implementation of lockdowns during the second half of March 2020. We show this decline had a heterogeneous impact by economic sector, firm size, and transaction type (in-person vs. online). Focusing on financial technology adoption by studying the migration from in-person to online banking transactions, we find that MSMEs managed to recover revenues to pre-pandemic levels in early 2021 thanks to an increased share of online transactions and that industries facing higher physical exposure to the public (e.g., retailers) experienced a more considerable decline and a slower recovery.
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Colombo, Karina, Elisa Failache et Martina Querejeta. High-Speed Internet and Socioemotional Wellbeing in Uruguayan Youth. Inter-American Development Bank, novembre 2023. http://dx.doi.org/10.18235/0005154.

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This paper analyses the causal effects of exposure to high-speed internet on socioemotional wellbeing in adolescence and youth. We exploit the geographic and cross-cohort differences in fiber optic accessibility given by the fiber-optic-to-the-home (FTTH) project developed in Uruguay in the period 2011-2018. We identify intention-to-treat effects by combining administrative data on FTTH rollout with large survey data specially designed to collect outcomes in youth. Our results show that access to high-speed internet has mixed effects on mental health. Going from 0 to a 100% probability in FTTH accessibility reduces the incidence of feeling lonely in 9 percentage points (pp) but increases the incidence of feeling worried in 9 pp. We also find an increase in the probability of having a medical visit in 10 pp, without statistically significant effects in visits to a psychologist or psychiatrist. Our results further evidence an increase in the probability of alcohol and marijuana consumption, showing that internet access can also affect risky behaviours. The analysis of heterogeneous effects by gender, age, region of residence, and educational background shows that, while the effect on feeling worried is observed across all sub-samples, the reduction in loneliness is mostly explained by boys, individuals under 18, and with lower educational background. Dissatisfaction with their way of being emerges as the leading mechanism behind the detrimental effect on feeling worried. We do not find any evidence on FTTH access displacing offline recreational activities at the extensive margin.
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van de Sand, Ron, et Jörg Reiff-Stephan. FrostByte Dataset. Technische Hochschule Wildau, 2021. http://dx.doi.org/10.15771/1894.

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It is with great pleasure that we announce the release of the “Frost Byte” dataset. The dataset was collected during research on fault detection and diagnosis (FDD) approaches and their transferability to heterogeneous systems of industrial refrigeration systems at the Technical University of Applied Sciences Wildau. The data collection took place between 2018 and 2019 and is intended to supplement the ASHRAE 1043-RP dataset (Comstock & Braun, 1999), which has been the only publicly available data source in this area to date. For comparison purposes, the method of data collection was chosen similarly, whereby this dataset collection was carried out using a ~ 100 kW refrigeration capacity ammonia system with plate heat exchangers. The dataset contains steady-data equally sampled from five classes: 1. Normal (fault-free operating condition) 2. Reduced Condenser Water Flow 3. Reduced Evaporator Water Flow 4. Non-Condensable Gases 5. Refrigeration Leak Each class was investigated under changing operational conditions and varying fault severity levels (SL).
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Murad, M. Hassan, Stephanie M. Chang, Celia Fiordalisi, Jennifer S. Lin, Timothy J. Wilt, Amy Tsou, Brian Leas et al. Improving the Utility of Evidence Synthesis for Decision Makers in the Face of Insufficient Evidence. Agency for Healthcare Research and Quality (AHRQ), avril 2021. http://dx.doi.org/10.23970/ahrqepcwhitepaperimproving.

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Background: Healthcare decision makers strive to operate on the best available evidence. The Agency for Healthcare Research and Quality Evidence-based Practice Center (EPC) Program aims to support healthcare decision makers by producing evidence reviews that rate the strength of evidence. However, the evidence base is often sparse or heterogeneous, or otherwise results in a high degree of uncertainty and insufficient evidence ratings. Objective: To identify and suggest strategies to make insufficient ratings in systematic reviews more actionable. Methods: A workgroup comprising EPC Program members convened throughout 2020. We conducted interative discussions considering information from three data sources: a literature review for relevant publications and frameworks, a review of a convenience sample of past systematic reviews conducted by the EPCs, and an audit of methods used in past EPC technical briefs. Results: Several themes emerged across the literature review, review of systematic reviews, and review of technical brief methods. In the purposive sample of 43 systematic reviews, the use of the term “insufficient” covered both instances of no evidence and instances of evidence being present but insufficient to estimate an effect. The results of the literature review and review of the EPC Program systematic reviews illustrated the importance of clearly stating the reasons for insufficient evidence. Results of both the literature review and review of systematic reviews highlighted the factors decision makers consider when making decisions when evidence of benefits or harms is insufficient, such as costs, values, preferences, and equity. We identified five strategies for supplementing systematic review findings when evidence on benefit or harms is expected to be or found to be insufficient, including: reconsidering eligible study designs, summarizing indirect evidence, summarizing contextual and implementation evidence, modelling, and incorporating unpublished health system data. Conclusion: Throughout early scoping, protocol development, review conduct, and review presentation, authors should consider five possible strategies to supplement potential insufficient findings of benefit or harms. When there is no evidence available for a specific outcome, reviewers should use a statement such as “no studies” instead of “insufficient.” The main reasons for insufficient evidence rating should be explicitly described.
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Kingston, A. W., A. Mort, C. Deblonde et O H Ardakani. Hydrogen sulfide (H2S) distribution in the Triassic Montney Formation of the Western Canadian Sedimentary Basin. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329797.

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The Montney Formation is a highly productive hydrocarbon reservoir with significant reserves of hydrocarbon gases and liquids making it of great economic importance to Canada. However, high concentrations of hydrogen sulfide (H2S) have been encountered during exploration and development that have detrimental effects on environmental, health, and economics of production. H2S is a highly toxic and corrosive gas and therefore it is essential to understand the distribution of H2S within the basin in order to enhance identification of areas with a high risk of encountering elevated H2S concentrations in order to mitigate against potential negative impacts. Gas composition data from Montney wells is routinely collected by operators for submission to provincial regulators and is publicly available. We have combined data from Alberta (AB) and British Columbia (BC) to create a basin-wide database of Montney H2S concentrations. We then used an iterative quality control and quality assurance process to produce a dataset that best represents gas composition in reservoir fluids. This included: 1) designating gas source formation based on directional surveys using a newly developed basin-wide 3D model incorporating AGS's Montney model of Alberta with a model in BC, which removes errors associated with reported formations; 2) removed injection and disposal wells; 3) assessed wells with the 50 highest H2S concentrations to determine if gas composition data is accurate and reflective of reservoir fluid chemistry; and 4) evaluated spatially isolated extreme values to ensure data accuracy and prevent isolated highs from negatively impacting data interpolation. The resulting dataset was then used to calculate statistics for each x, y location to input into the interpolation process. Three interpolations were constructed based on the associated phase classification: H2S in gas, H2S in liquid (C7+), and aqueous H2S. We used Empirical Bayesian Kriging interpolation to generate H2S distribution maps along with a series of model uncertainty maps. These interpolations illustrate that H2S is heterogeneously distributed across the Montney basin. In general, higher concentrations are found in AB compared with BC with the highest concentrations in the Grande Prairie region along with several other isolated region in the southeastern portion of the basin. The interpolations of H2S associated with different phases show broad similarities. Future mapping research will focus on subdividing intra-Montney sub-members plus under- and overlying strata to further our understanding of the role migration plays in H2S distribution within the Montney basin.
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Shpigel, Nahum Y., Ynte Schukken et Ilan Rosenshine. Identification of genes involved in virulence of Escherichia coli mastitis by signature tagged mutagenesis. United States Department of Agriculture, janvier 2014. http://dx.doi.org/10.32747/2014.7699853.bard.

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Mastitis, an inflammatory response of the mammary tissue to invading pathogenic bacteria, is the largest health problem in the dairy industry and is responsible for multibillion dollar economic losses. E. coli are a leading cause of acute mastitis in dairy animals worldwide and certainly in Israel and North America. The species E. coli comprises a highly heterogeneous group of pathogens, some of which are commensal residents of the gut, infecting the mammary gland after contamination of the teat skin from the environment. As compared to other gut microflora, mammary pathogenic E. coli (MPEC) may have undergone evolutionary adaptations that improve their fitness for colonization of the unique and varied environmental niches found within the mammary gland. These niches include competing microbes already present or accompanying the new colonizer, soluble and cellular antimicrobials in milk, and the innate immune response elicited by mammary cells and recruited immune cells. However, to date, no specific virulence factors have been identified in E. coli isolates associated with mastitis. The original overall research objective of this application was to develop a genome-wide, transposon-tagged mutant collection of MPEC strain P4 and to use this technology to identify E. coli genes that are specifically involved in mammary virulence and pathogenicity. In the course of the project we decided to take an alternative genome-wide approach and to use whole genomes bioinformatics analysis. Using genome sequencing and analysis of six MPEC strains, our studies have shown that type VI secretion system (T6SS) gene clusters were present in all these strains. Furthermore, using unbiased screening of MPEC strains for reduced colonization, fitness and virulence in the murine mastitis model, we have identified in MPEC P4-NR a new pathogenicity island (PAI-1) encoding the core components of T6SS and its hallmark effectors Hcp, VgrG and Rhs. Next, we have shown that specific deletions of T6SS genes reduced colonization, fitness and virulence in lactating mouse mammary glands. Our long-term goal is to understand the molecular mechanisms of host-pathogen interactions in the mammary gland and to relate these mechanisms to disease processes and pathogenesis. We have been able to achieve our research objectives to identify E. coli genes that are specifically involved in mammary virulence and pathogenicity. The project elucidated a new basic concept in host pathogen interaction of MPEC, which for the best of our knowledge was never described or investigated before. This research will help us to shed new light on principles behind the infection strategy of MPEC. The new targets now enable prevalence and epidemiology studies of T6SS in field strains of MPEC which might unveil new geographic, management and ecological risk factors. These will contribute to development of new approaches to treat and prevent mastitis by MPEC and perhaps other mammary pathogens. The use of antibiotics in farm animals and specifically to treat mastitis is gradually precluded and thus new treatment and prevention strategies are needed. Effective mastitis vaccines are currently not available, structural components and effectors of T6SS might be new targets for the development of novel vaccines and therapeutics.
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