Conceptual Graphs Based Information Retrieval in HealthAgents

This paper focuses on the problem of representing, in a meaningful way, the knowledge involved in the HealthAgents project. Our work is motivated by the complexity of representing Electronic Health-care Records in a consistent manner. We present HADOM (HealthAgents Domain Ontology) which conceptualises the required HealthAgents information and propose describing the sources knowledge by the means of Conceptual Graphs (CGs). This allows to build upon the existing ontology permitting for modularity and °exibility. The novelty of our approach lies in the ease with which CGs can be placed above other formalisms and their potential for optimised querying and retrieval

Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues

We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually expressed as fractions of the confusion matrix, such as sensitivity, specificity, negative and positive predictive values. We extend this concept to soft classification and discuss the bias and variance properties of the extended performance measures. Ambiguity in reference labels translates to differences between best-case, expected and worst-case performance. We show a second set of measures comparing expected and ideal performance which is closely related to regression performance, namely the root mean squared error RMSE and the mean absolute error MAE. All calculations apply to classical crisp classification as well as to soft classification (partial class memberships and/or one-class classifiers). The proposed performance measures allow to test classifiers with actual borderline cases. In addition, hardening of e.g. posterior probabilities into class labels is not necessary, avoiding the corresponding information loss and increase in variance. We implement the proposed performance measures in the R package "softclassval", which is available from CRAN and at http://softclassval.r-forge.r-project.org. Our reasoning as well as the importance of partial memberships for chemometric classification is illustrated by a real-word application: astrocytoma brain tumor tissue grading (80 patients, 37000 spectra) for finding surgical excision borders. As borderline cases are the actual target of the analytical technique, samples which are diagnosed to be borderline cases must be included in the validation.Comment: The manuscript is accepted for publication in Chemometrics and Intelligent Laboratory Systems. Supplementary figures and tables are at the end of the pd

A graph-based approach for the retrieval of multi-modality medical images

Medical imaging has revolutionised modern medicine and is now an integral aspect of diagnosis and patient monitoring. The development of new imaging devices for a wide variety of clinical cases has spurred an increase in the data volume acquired in hospitals. These large data collections offer opportunities for search-based applications in evidence-based diagnosis, education, and biomedical research. However, conventional search methods that operate upon manual annotations are not feasible for this data volume. Content-based image retrieval (CBIR) is an image search technique that uses automatically derived visual features as search criteria and has demonstrable clinical benefits. However, very few studies have investigated the CBIR of multi-modality medical images, which are making a monumental impact in healthcare, e.g., combined positron emission tomography and computed tomography (PET-CT) for cancer diagnosis. In this thesis, we propose a new graph-based method for the CBIR of multi-modality medical images. We derive a graph representation that emphasises the spatial relationships between modalities by structurally constraining the graph based on image features, e.g., spatial proximity of tumours and organs. We also introduce a graph similarity calculation algorithm that prioritises the relationships between tumours and related organs. To enable effective human interpretation of retrieved multi-modality images, we also present a user interface that displays graph abstractions alongside complex multi-modality images. Our results demonstrated that our method achieved a high precision when retrieving images on the basis of tumour location within organs. The evaluation of our proposed UI design by user surveys revealed that it improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The work in this thesis advances the state-of-the-art by enabling a novel approach for the retrieval of multi-modality medical images

Exploring and linking biomedical resources through multidimensional semantic spaces

Background The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). Results This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. Conclusions Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces

To assess the effectiveness of tailored food recipe in attenuating the progression of cancer cachexia to refractory cachexia in adult female patients undergoing palliative care in India

Cancer cachexia negatively impacts patients’ capability to undergo chemotherapy and fight infection. Increased energy expenditure and anorexia are key clinical features among cachexia patients leading to body weight loss. Therefore, it is imperative to assess all cancer patients for early signs of undernourishment. Nutrition intervention with counseling may ameliorate undernutrition and metabolic alterations. The aim of this study was to attenuate the progression to refractory cachexia, improve nutritional status and quality of life of female palliative care patients by providing nutrient rich natural food along with counseling. Female cancer patients with symptoms of cachexia were randomly distributed into control and intervention group. Patients were recruited from the Palliative clinic, Oncology department in AIIMS, New Delhi, India; control/placebo groups (for pilot n= 30 and scale-up n=75) and intervention groups (for pilot n= 33 and scale-up n=75). In addition to nutritional and physical activity counseling, intervention patients were instructed to consume 100g nutritional supplement (IAtta) on a daily basis with their normal dietary intake for a six month period, during the pilot study. Moreover, during the scale-up study, the intervention group received 100g of IAtta while the placebo group received 100g of whole wheat flour for daily consumption. Anthropometric measurements, physical activity level (PAL), dietary intake, quality of life (QoL) and biochemical indices were assessed at baseline, three-month and after six-month period. Study variables were analysed using repeated-measures ANOVA and the Friedman test for multi–comparisons to determine the changes within the groups at different time points (i.e. baseline, mid-intervention and post-intervention). Student t-test/ Wilcoxon ranksum tests were performed on the variables to assess the difference between the intervention and control/placebo groups at baseline (P- value ≤0.05; 95% confidence interval). After six months, patients in intervention group (IAtta group) had significant improvement in PAL (p<0.001) and QoL domain (global health status, p<0.001 and fatigue, p=0.001). Conversely, the QoL in the placebo group did not improve (global health status, p=0.74) nor did PAL (p=0.49). Body mass index was maintained in both groups (IAtta, p-value 0.121; Placebo, p-value 0.35). Serum albumin levels were significantly reduced (p = 0.005) in placebo group patients after six months of intervention. Nutrition sensitive intervention (IAtta meal) along with counseling (tailored nutrition and physical activity) improves quality of life and nutritional status as well as delays progression of cachexia among female palliative care patients. These findings highlight the need to ascertain the nutritional status of cancer patients and underpin the pivotal role of IAtta as intervention tool to compensate for deficient nutrients. It is therefore suggested to embed IAtta into the Indian palliative care framework to modulate cancer progression

Investigating the role of machine learning and deep learning techniques in medical image segmentation

openThis work originates from the growing interest of the medical imaging community in the application of machine learning techniques and, from deep learning to improve the accuracy of cancerscreening. The thesis is structured into two different tasks. In the first part, magnetic resonance images were analysed in order to support clinical experts in the treatment of patients with brain tumour metastases (BM). The main topic related to this study was to investigate whether BM segmentation may be approached successfully by two supervised ML classifiers belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural Network model are selected from the literature as representative of the two approaches. The second task related to this thesisis illustrated the development of a deep learning study aimed to process and classify lesions in mammograms with the use of slender neural networks. Mammography has a central role in screening and diagnosis of breast lesions. Deep Convolutional Neural Networks have shown a great potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and reproducibility. A traditional convolution network was compared with a novel one obtained making use of much more efficient depth wise separable convolution layers. As a final goal to integrate the system developed in clinical practice, for both fields studied, all the Medical Imaging and Pattern Recognition algorithmic solutions have been integrated into a MATLAB® software packageopenInformatica e matematica del calcologonella gloriaGonella, Glori