896 research outputs found
Bayesian Network Structure Learning with Permutation Tests
In literature there are several studies on the performance of Bayesian
network structure learning algorithms. The focus of these studies is almost
always the heuristics the learning algorithms are based on, i.e. the
maximisation algorithms (in score-based algorithms) or the techniques for
learning the dependencies of each variable (in constraint-based algorithms). In
this paper we investigate how the use of permutation tests instead of
parametric ones affects the performance of Bayesian network structure learning
from discrete data. Shrinkage tests are also covered to provide a broad
overview of the techniques developed in current literature.Comment: 13 pages, 4 figures. Presented at the Conference 'Statistics for
Complex Problems', Padova, June 15, 201
Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images
A method for segmenting water bodies in optical and synthetic aperture radar
(SAR) satellite images is proposed. It makes use of the textural features of
the different regions in the image for segmentation. The method consists in a
multiscale analysis of the images, which allows us to study the images
regularity both, locally and globally. As results of the analysis, coarse
multifractal spectra of studied images and a group of images that associates
each position (pixel) with its corresponding value of local regularity (or
singularity) spectrum are obtained. Thresholds are then applied to the
multifractal spectra of the images for the classification. These thresholds are
selected after studying the characteristics of the spectra under the assumption
that water bodies have larger local regularity than other soil types.
Classifications obtained by the multifractal method are compared quantitatively
with those obtained by neural networks trained to classify the pixels of the
images in covered against uncovered by water. In optical images, the
classifications are also compared with those derived using the so-called
Normalized Differential Water Index (NDWI)
Strategic research agenda for biomedical imaging
This Strategic Research Agenda identifies current challenges and needs in healthcare, illustrates how biomedical imaging and derived data can help to address these, and aims to stimulate dedicated research funding efforts. Medicine is currently moving towards a more tailored, patient-centric approach by providing personalised solutions for the individual patient. Innovation in biomedical imaging plays a key role in this process as it addresses the current needs for individualised prevention, treatment, therapy response monitoring, and image-guided surgery. The use of non-invasive biomarkers facilitates better therapy prediction and monitoring, leading to improved patient outcomes. Innovative diagnostic imaging technologies provide information about disease characteristics which, coupled with biological, genetic and -omics data, will contribute to an individualised diagnosis and therapy approach. In the emerging field of theranostics, imaging tools together with therapeutic agents enable the selection of best treatments and allow tailored therapeutic interventions. For prenatal monitoring, the use of innovative imaging technologies can ensure an early detection of malfunctions or disease. The application of biomedical imaging for diagnosis and management of lifestyle-induced diseases will help to avoid disease development through lifestyle changes. Artificial intelligence and machine learning in imaging will facilitate the improvement of image interpretation and lead to better disease prediction and therapy planning. As biomedical imaging technologies and analysis of existing imaging data provide solutions to current challenges and needs in healthcare, appropriate funding for dedicated research is needed to implement the innovative approaches for the wellbeing of citizens and patients.</p
Automatic production and integration of knowledge to the support of the decision and planning activities in medical-clinical diagnosis, treatment and prognosis.
El concepto de procedimiento médico se refiere al conjunto de actividades seguidas por los profesionales de la salud para solucionar o mitigar el problema de salud que afecta a un paciente. La toma de decisiones dentro del procedimiento médico ha sido, por largo tiempo, uno de las áreas más interesantes de investigación en la informática médica y el contexto de investigación de esta tesis. La motivación para desarrollar este trabajo de investigación se basa en tres aspectos fundamentales: no hay modelos de conocimiento para todas las actividades médico-clínicas que puedan ser inducidas a partir de datos médicos, no hay soluciones de aprendizaje inductivo para todas las actividades de la asistencia médica y no hay un modelo integral que formalice el concepto de procedimiento médico. Por tanto, nuestro objetivo principal es desarrollar un modelo computable basado en conocimiento que integre todas las actividades de decisión y planificación para el diagnóstico, tratamiento y pronóstico médico-clínicos.
Para alcanzar el objetivo principal, en primer lugar, explicamos el problema de investigación. En segundo lugar, describimos los antecedentes del problema de investigación desde los contextos médico e informático. En tercer lugar, explicamos el desarrollo de la propuesta de investigación, basada en cuatro contribuciones principales: un nuevo modelo, basado en datos y conocimiento, para la actividad de planificación en el diagnóstico y tratamiento médico-clínicos; una novedosa metodología de aprendizaje inductivo para la actividad de planificación en el diagnóstico y tratamiento médico-clínico; una novedosa metodología de aprendizaje inductivo para la actividad de decisión en el pronóstico médico-clínico, y finalmente, un nuevo modelo computable, basado en datos y conocimiento, que integra las actividades de decisión y planificación para el diagnóstico, tratamiento y pronóstico médico-clínicos.The concept of medical procedure refers to the set of activities carried out by the health care professionals to solve or mitigate the health problems that affect a patient. Decisions making within a medical procedure has been, for a long time, one of the most interesting research areas in medical informatics and the research context of this thesis. The motivation to develop this research work is based on three main aspects: Nowadays there are not knowledge models for all the medical-clinical activities that can be induced from medical data, there are not inductive learning solutions for all the medical-clinical activities, and there is not an integral model that formalizes the concept of medical procedure. Therefore, our main objective is to develop a computable model based in knowledge that integrates all the decision and planning activities for the medical-clinical diagnosis, treatment and prognosis.
To achieve this main objective: first, we explain the research problem. Second, we describe the background of the work from both the medical and the informatics contexts. Third, we explain the development of the research proposal based on four main contributions: a novel knowledge representation model, based in data, to the planning activity in medical-clinical diagnosis and treatment; a novel inductive learning methodology to the planning activity in diagnosis and medical-clinical treatment; a novel inductive learning methodology to the decision activity in medical-clinical prognosis, and finally, a novel computable model, based on data and knowledge, which integrates the
decision and planning activities of medical-clinical diagnosis, treatment and prognosis
On Identifying Significant Edges in Graphical Models of Molecular Networks
Objective: Modelling the associations from high-throughput experimental
molecular data has provided unprecedented insights into biological pathways and
signalling mechanisms. Graphical models and networks have especially proven to
be useful abstractions in this regard. Ad-hoc thresholds are often used in
conjunction with structure learning algorithms to determine significant
associations. The present study overcomes this limitation by proposing a
statistically-motivated approach for identifying significant associations in a
network.
Methods and Materials: A new method that identifies significant associations
in graphical models by estimating the threshold minimising the
norm between the cumulative distribution function (CDF) of the observed edge
confidences and those of its asymptotic counterpart is proposed. The
effectiveness of the proposed method is demonstrated on popular synthetic data
sets as well as publicly available experimental molecular data corresponding to
gene and protein expression profiles.
Results: The improved performance of the proposed approach is demonstrated
across the synthetic data sets using sensitivity, specificity and accuracy as
performance metrics. The results are also demonstrated across varying sample
sizes and three different structure learning algorithms with widely varying
assumptions. In all cases, the proposed approach has specificity and accuracy
close to 1, while sensitivity increases linearly in the logarithm of the sample
size. The estimated threshold systematically outperforms common ad-hoc ones in
terms of sensitivity while maintaining comparable levels of specificity and
accuracy. Networks from experimental data sets are reconstructed accurately
with respect to the results from the original papers.Comment: 21 pages, 9 figures. Presented at the Conference for Artificial
Intelligence in Medicine (AIME '11), Workshop on Probabilistic Problem
Solving in Biomedicin
An Aboriginal English Ontology Framework for Patient-Practitioner Interview Encounters
Current diagnosis, treatment and healthcare delivery processes in Australia are dominated by long established westernized clinically driven methods of patient-practitioner interaction. Consequently this dominant healthcare provider influence contributes to risk of miscommunication, misinformation in patient records and reciprocal misunderstandings that go unrecognised as such. For Indigenous communities, inadequate health literacy (HL) and a pervasive semantic disconnect are major barriers. Overcoming these barriers in the primary care setting presents opportunities to deliver appropriate timely and more effective care. We propose an e-health framework that enhances the Patient-Practitioner Interview Encounter (PPIE) through the use of a patient-centric linguistic interface using semantic mappings between Aboriginal English (AE) and Standard Australian English (SAE). This will ameliorate communications and interactions, so meeting the needs of all stakeholders (Patients, Physicians, Nurses, Allied Health Professionals and their Non-Critical Carers) engaged in Indigenous patient-centric primary care. It provides healthcare practitioners and their Indigenous T2DM patients with a new platform for two-way educative sharing and knowledge exchange that will increase mutually productive treatment, care and management expectations
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