Search

[EN] Mobile health (m-health) apps can bring health prevention and promotion to the general population. The main purpose of this article is to analyze different m-health apps for a broad spectrum of consumers by means of three different experiences. This goal was defined following the strategic documents generated by the main prospective observatories of Information and Communications Technology for health. After a general exploration of the app markets, we analyze the entries of three specific themes focused in this article: type 2 diabetes, obesity, and breast-feeding. The user experiences reported in this study mostly cover the segments of (1) chronically monitored consumers through a Web mobile app for predicting type 2 diabetes (Diab_Alert app), (2) information seekers through a mobile app for maternity (Lactation app) and partially (3) the motivated healthy consumers through a mobile app for a dietetic monitoring and assessment (SapoFit app). These apps were developed by the authors of this work.

The current challenge in automatic brain tumor classification based on MRS is the improvement of the robustness of the classification models that explicitly account for the probable breach of the independent and identically distributed conditions in the MRS data points. To contribute to this purpose, a new algorithm for the extraction of discriminant MRS features of brain tumors based on a functional approach is presented. Functional data analysis based on region segmentation (RSFDA) is based on the functional data analysis formalism using nonuniformly distributed B splines according to spectral regions that are highly correlated. An exhaustive characterization of the method is presented in this work using controlled and real scenarios. The performance of RSFDA was compared with other widely used feature extraction methods. In all simulated conditions, RSFDA was proven to be stable with respect to the number of variables selected and with respect to the classification performance against noise and baseline artifacts. Furthermore, with real multicenter datasets classification, RSFDA and peak integration (PI) obtained better performance than the other feature extraction methods used for comparison. Other advantages of the method proposed are its usefulness in selecting the optimal number of features for classification and its simplified functional representation of the spectra, which contributes to highlight the discriminative regions of the MR spectrum for each classification task. © 2012 John Wiley & Sons, Ltd.

Aims: To evaluate the accuracy of single-voxel Magnetic Resonance Spectroscopy (1H-MRS) as a non-invasive diagnostic aid for pediatric brain tumours in a multi-national study. Our hypotheses are (1) that automated classification based on 1H-MRS provides an accurate non-invasive diagnosis in multi-centre datasets and (2) using a protocol which increases the metabolite information improves the diagnostic accuracy. Methods: 78 patients under 16 years old with histologically proven brain tumours from 10 international centres were investigated. Discrimination of 29 medulloblastomas, 11 ependymomas and 38 pilocytic astrocytomas was evaluated. Single-voxel MRS was undertaken prior to diagnosis (1.5Tesla PRESS, PROBE or STEAM, TE 20-32 ms, and 135-136 ms). MRS data was processed using two strategies, determination of metabolite concentrations using TARQUIN software and automatic feature extraction with Peak Integration. Linear Discriminant Analysis was applied to this data to produce diagnostic classifiers. An evaluation of the diagnostic accuracy was performed based on resampling to measure the Balanced Accuracy Rate (BAR). Results: The accuracy of the diagnostic classifiers for discriminating the three tumour types was found to be high (BAR 0.98) when a combination of TE was used. The combination of both TE significantly improved the classification performance (p < 0.01, Tukey¿s test) compared with the use of one TE alone. 3 Other tumour types were classified accurately as glial or primitive neuroectodermal (BAR 1.00). Conclusions: 1H-MRS has excellent accuracy for the non-invasive diagnosis of common childhood brain tumours particularly if the metabolite information is maximised and should become part of routine clinical assessment for these children.

The success of Clinical Decision Support Systems (CDSS) greatly depends on its capability of being integrated in Health Information Systems (HIS). Several proposals have been published up to date to permit CDSS gathering patient data from HIS. Some base the CDSS data input on the HL7 reference model, however, they are tailored to specific CDSS or clinical guidelines technologies, or do not focus on standardizing the CDSS resultant knowledge. We propose a solution for facilitating semantic interoperability to rule-based CDSS focusing on standardized input and output documents conforming an HL7-CDA wrapper. We define the HL7-CDA restrictions in a HL7-CDA implementation guide. Patient data and rule inference results are mapped respectively to and from the CDSS by means of a binding method based on an XML binding file. As an independent clinical document, the results of a CDSS can present clinical and legal validity. The proposed solution is being applied in a CDSS for providing patient-specific recommendations for the care management of outpatients with diabetes mellitus.

A lo largo de las últimas décadas, la disponibilidad cada vez mayor de grandes cantidades de información biomédica ha potenciado el desarrollo de herramientas que permiten extraer e inferir conocimiento. El aumento de tecnologías biomédicas que asisten a los expertos médicos en sus decisiones ha contribuido a la incorporación de un paradigma de medicina basada en la evidencia centrada en el paciente. Las contribuciones de esta Tesis se centran en el desarrollo de herramientas que asisten al médico en su proceso de toma de decisiones en el diagnóstico de tumores cerebrales (TC) mediante Espectros de Resonancia Magnética (ERM). En esta Tesis se contribuye con el desarrollo de clasificadores basados en Reconocimiento de Patrones (RP) entrenados con ERM de pacientes pediátricos y adultos para establecer el tipo y grado del tumor. Estos clasificadores especializados son capaces de aprovechar las diferencias bioquímicas existentes entre los TC infantiles y de adultos. Una de las principales contribuciones de esta Tesis consiste en el desarrollo de modelos de clasificación enfocados a discriminar los tres tipos de tumores cerebrales pediátricos más prevalentes. El cerebelo suele ser una localización habitual de estos tumores, resultando muy difícil distinguir el tipo mediante el uso de Imagen de Resonancia Magnética. Por lo tanto, obtener un alto acierto en la discriminación de astrocitomas pilocíticos, ependimomas y meduloblastomas mediante ERM resulta crucial para establecer una estrategia de cirugía, ya que cada tipo de tumor requiere de unas acciones diferentes si se quiere obtener un buen pronóstico del paciente. Asimismo, esta Tesis contribuye en la extracción de características para ERM mediante el estudio de la combinación de señales de ERM adquiridas en dos tiempos de eco: tiempo de eco corto y tiempo de eco largo; concluyendo que dicha combinación mejora la clasificación de tumores cerebrales pediátricos frente al hecho de usar únicamente los ERM de un

[EN] New biomedical technologies enable the diagnosis of brain tumours by using non-invasive methods. HealthAgents is a European Union-funded research project that aims to build an agent-based distributed decision support system (dDSS) for the diagnosis of brain tumours. This is achieved using the latest biomedical knowledge, information and communication technologies and pattern recognition (PR) techniques. As part of the PR development of HealthAgents, an independent and automatic classification framework (CF) has been developed. This framework has been integrated with the HealthAgents dDSS using the HealthAgents agent platform. The system offers (1) the functionality to search for distributed classifiers to solve specific questions; (2) automatic classification of new cases; (3) instant deployment of new validated classifiers; and (4) the ability to rank a set of classifiers according to their performance and suitability for the case in hand. The CF enables both the deployment of new classifiers using the provided Extensible Markup Language(1) classifier specification, and the inclusion of new PR techniques that make the system extensible. These features may enable the rapid integration of PR laboratory results into industrial or research applications, such as the HealthAgents dDSS. Two classification nodes have been deployed and they currently offer classification services by means of dedicated servers connected to the HealthAgents agent platform: one node being located at the Katholieke Universiteit Leuven, Belgium and the other at the Universidad Politecnica de Valencia, Spain. These classification nodes share the current set of brain tumour classifiers that have been trained from in vivo magnetic resonance spectroscopy data. The combination of the CF with a distributed agent system constitutes the basis of the brain tumour dDSS developed in HealthAgents.

The final publication is available at Springer via http://dx.doi.org/10.1007/s10334-010-0241-8, Object: This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases. Materials and methods: Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed. Results: Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns. Conclusion: These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T 1H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments. © 2011 ESMRMB.

In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally when new data are collected. In this study, an incremental learning algorithm for Gaussian Discriminant Analysis (iGDA) based on the Graybill and Deal weighted combination of estimators is introduced. Each time a new set of data becomes available, a new estimation is carried out and a combination with a previous estimation is performed. iGDA does not require access to the previously used data and is able to include new classes that were not in the original analysis, thus allowing the customization of the models to the distribution of data at a particular clinical center. An evaluation using five benchmark databases has been used to evaluate the behaviour of the iGDA algorithm in terms of stability-plasticity, class inclusion and order effect. Finally, the iGDA algorithm has been applied to automatic brain tumour classification with magnetic resonance spectroscopy, and compared with two state-of-the-art incremental algorithms. The empirical results obtained show the ability of the algorithm to learn in an incremental fashion, improving the performance of the models when new information is available, and converging in the course of time. Furthermore, the algorithm shows a negligible instance and concept order effect, avoiding the bias that such effects could introduce. © 2011 Elsev

[EN] Automatic brain tumor classification by MRS has been under development for more than a decade. Nonetheless, to our knowledge, there are no published evaluations of predictive models with unseen cases that are subsequently acquired in different centers. The multicenter eTUMOUR project (2004-2009), which builds upon previous expertise from the INTERPRET project (2000-2002) has allowed such an evaluation to take place. A total of 253 pairwise classifiers for glioblastoma, meningioma, metastasis, and low-grade glial diagnosis were inferred based on 211 SV short TE INTERPRET MR spectra obtained at 1.5 T (PRESS or STEAM, 20-32 ms) and automatically pre-processed. Afterwards, the classifiers were tested with 97 spectra, which were subsequently compiled during eTUMOUR. In our results based on subsequently acquired spectra, accuracies of around 90% were achieved for most of the pairwise discrimination problems. The exception was for the glioblastoma versus metastasis discrimination, which was below 78%. A more clear definition of metastases may be obtained by other approaches, such as MRSI + MRI. The prediction of the tumor type of in-vivo MRS is possible using classifiers developed from previously acquired data, in different hospitals with different instrumentation under the same acquisition protocols. This methodology may find application for assisting in the diagnosis of new brain tumor cases and for the quality control of multicenter MRS databases.

[EN] In this paper, a new method for modelling tRNA secondary structures is presented. This method is based on the combination of stochastic context-free grammars (SCFG) and Hidden Markov Models (HMM). HMM are used to capture the local relations in the loops of the molecule (nonstructured regions) and SCFG are used to capture the long term relations between nucleotides of the arms (structured regions). Given annotated public databases, the HMM and SCFG models are learned by means of automatic inductive learning methods. Two SCFG learning methods have been explored. Both of them take advantage of the structural information associated with the training sequences: one of them is based on a stochastic version of the Sakakibara algorithm and the other one is based on a Corpus based algorithm. A final model is then obtained by merging of the HMM of the nonstructured regions and the SCFG of the structured regions. Finally, the performed experiments on the tRNA sequence corpus and the non-tRNA sequence corpus give significant results. Comparative experiments with another published method are also presented.