Case-Based Statistical Learning: A Non-Parametric Implementation with a Conditional-Error Rate SVM

© 2013 IEEE. Machine learning has been successfully applied to many areas of science and engineering. Some examples include time series prediction, optical character recognition, signal and image classification in biomedical applications for diagnosis and prognosis and so on. In the theory of semi-supervised learning, we have a training set and an unlabeled data, that are employed to fit a prediction model or learner, with the help of an iterative algorithm, such as the expectation-maximization algorithm. In this paper, a novel non-parametric approach of the so-called case-based statistical learning is proposed in a low-dimensional classification problem. This supervised feature selection scheme analyzes the discrete set of outcomes in the classification problem by hypothesis-testing and makes assumptions on these outcome values to obtain the most likely prediction model at the training stage. A novel prediction model is described in terms of the output scores of a confidence-based support vector machine classifier under class-hypothesis testing. To have a more accurate prediction by considering the unlabeled points, the distribution of unlabeled examples must be relevant for the classification problem. The estimation of the error rates from a well-trained support vector machines allows us to propose a non-parametric approach avoiding the use of Gaussian density function-based models in the likelihood ratio test

Characterization of Patients with Chronic Diseases and Complex Care Needs: A New High-Risk Emergent Population

Background: To analyze the prevalence and main epidemiological, clinical and outcome features of in-Patients with Complex Chronic conditions (PCC) in internal medicine areas, using a pragmatic working definition. Methods: Prospective study in 17 centers from Spain, with 97 in-hospital, monthly prevalence cuts. A PCC was considered when criteria of polypathological patient (two or more major chronic diseases) were met, or when a patient suffered one major chronic disease plus one or more of nine predefined complexity criteria like socio-familial risk, alcoholism or malnutrition among others (PCC without polypathology). A complete set of baseline features as well as 12-months survival were collected. Then, we compared clinical, outcome variables, and PROFUND index accuracy between polypathological patients and PCC without polypathology. Results: The global prevalence of PCC was 61% (40% of them were polypathological patients, and 21% PCC withouth polypathology) out of the 2178 evaluated patients. Their median age was 82 (59.5% men), suffered 2.3 ± 1.1 major diseases (heart diseases (70.5%), neurologic (41.5%), renal (36%), and lung diseases (26%)), 5.5 ± 2.5 other chronic conditions, met 2.5 ± 1.5 complexity criteria, and presented functional decline (Barthel index 55 (25-90)). Compared to polypathological patients, the subgroup of PCC without polypathology were younger, with a different pattern of major diseases and comorbidities, a better functional status, and lower 12-months mortality rates ((36.2% vs 46.8%; p = .003; OR 0.7(0.48-0.86). The PROFUND index obtained adequate calibration and discrimination power (AUC-ROC 0.67 (0.63-0.69)) in predicting 12-month mortality of PCC. Conclusion: Patients with complex chronic conditions are highly prevalent in internal medicine areas; their clinical pattern has changed in parallel to socio-epidemiological modifications, but their death-risk is still adequately predicted by PROFUND index

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Automatic ROI Selection in Structural Brain MRI Using SOM 3D Projection

This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.This work was partly supported by the MICINN under the TEC2012-34306 project and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Projects P09-TIC-4530 and P11-TIC-7103. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRxResearch; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Global Metabolomic Profiling of Acute Myocarditis Caused by Trypanosoma cruzi Infection

© 2014 Gironès et al. Chagas disease is caused by Trypanosoma cruzi infection, being cardiomyopathy the more frequent manifestation. New chemotherapeutic drugs are needed but there are no good biomarkers for monitoring treatment efficacy. There is growing evidence linking immune response and metabolism in inflammatory processes and specifically in Chagas disease. Thus, some metabolites are able to enhance and/or inhibit the immune response. Metabolite levels found in the host during an ongoing infection could provide valuable information on the pathogenesis and/or identify deregulated metabolic pathway that can be potential candidates for treatment and being potential specific biomarkers of the disease. To gain more insight into those aspects in Chagas disease, we performed an unprecedented metabolomic analysis in heart and plasma of mice infected with T. cruzi. Many metabolic pathways were profoundly affected by T. cruzi infection, such as glucose uptake, sorbitol pathway, fatty acid and phospholipid synthesis that were increased in heart tissue but decreased in plasma. Tricarboxylic acid cycle was decreased in heart tissue and plasma whereas reactive oxygen species production and uric acid formation were also deeply increased in infected hearts suggesting a stressful condition in the heart. While specific metabolites allantoin, kynurenine and p-cresol sulfate, resulting from nucleotide, tryptophan and phenylalanine/tyrosine metabolism, respectively, were increased in heart tissue and also in plasma. These results provide new valuable information on the pathogenesis of acute Chagas disease, unravel several new metabolic pathways susceptible of clinical management and identify metabolites useful as potential specific biomarkers for monitoring treatment and clinical severity in patients.This work was supported by ‘‘Ministerio de Ciencia e Innovación’’ (SAF2010-17833); ‘‘Fondo de Investigaciones Sanitarias’’ (PS09/00538 and PI12/00289); ‘‘Red de Investigación de Centros de Enfermedades Tropicales’’ (RICET RD12/0018/0004); European Union (HEALTH-FE-2008-22303, ChagasEpiNet);‘‘Universidad Autónoma de Madrid’’ and ‘‘Comunidad de Madrid’’ (CC08-UAM/SAL-4440/08); AECID Cooperation with Argentine (A/025417/09 and A/031735/10), Comunidad de Madrid (S-2010/BMD-2332) and ‘‘Fundación Ramón Areces’Peer Reviewe

Target Region Selection Is a Critical Determinant of Community Fingerprints Generated by 16S Pyrosequencing

Pyrosequencing of 16S rRNA genes allows for in-depth characterization of complex microbial communities. Although it is known that primer selection can influence the profile of a community generated by sequencing, the extent and severity of this bias on deep-sequencing methodologies is not well elucidated. We tested the hypothesis that the hypervariable region targeted for sequencing and primer degeneracy play important roles in influencing the composition of 16S pyrotag communities. Subgingival plaque from deep sites of current smokers with chronic periodontitis was analyzed using Sanger sequencing and pyrosequencing using 4 primer pairs. Greater numbers of species were detected by pyrosequencing than by Sanger sequencing. Rare taxa constituted nearly 6% of each pyrotag community and less than 1% of the Sanger sequencing community. However, the different target regions selected for pyrosequencing did not demonstrate a significant difference in the number of rare and abundant taxa detected. The genera Prevotella, Fusobacterium, Streptococcus, Granulicatella, Bacteroides, Porphyromonas and Treponema were abundant when the V1–V3 region was targeted, while Streptococcus, Treponema, Prevotella, Eubacterium, Porphyromonas, Campylobacer and Enterococcus predominated in the community generated by V4–V6 primers, and the most numerous genera in the V7–V9 community were Veillonella, Streptococcus, Eubacterium, Enterococcus, Treponema, Catonella and Selenomonas. Targeting the V4–V6 region failed to detect the genus Fusobacterium, while the taxa Selenomonas, TM7 and Mycoplasma were not detected by the V7–V9 primer pairs. The communities generated by degenerate and non-degenerate primers did not demonstrate significant differences. Averaging the community fingerprints generated by V1–V3 and V7–V9 primers providesd results similar to Sanger sequencing, while allowing a significantly greater depth of coverage than is possible with Sanger sequencing. It is therefore important to use primers targeted to these two regions of the 16S rRNA gene in all deep-sequencing efforts to obtain representational characterization of complex microbial communities

Association of Atopobium vaginae, a recently described metronidazole resistant anaerobe, with bacterial vaginosis

BACKGROUND: Bacterial vaginosis (BV) is a polymicrobial syndrome characterized by a change in vaginal flora away from predominantly Lactobacillus species. The cause of BV is unknown, but the condition has been implicated in diverse medical outcomes. The bacterium Atopobium vaginae has been recognized only recently. It is not readily identified by commercial diagnostic kits. Its clinical significance is unknown but it has recently been isolated from a tuboovarian abcess. METHODS: Nucleotide sequencing of PCR amplified 16S rRNA gene segments, that were separated into bands within lanes on polyacrylamide gels by denaturing gradient gel electrophoresis (DGGE), was used to examine bacterial vaginal flora in 46 patients clinically described as having normal (Lactobacillus spp. predominant; Nugent score ≤ 3) and abnormal flora (Nugent score ≥ 4). These women ranged in age from 14 to 48 and 82% were African American. RESULTS: The DGGE banding patterns of normal and BV-positive patients were recognizably distinct. Those of normal patients contained 1 to 4 bands that were focused in the centre region of the gel lane, while those of BV positive patients contained bands that were not all focused in the center region of the gel lane. More detailed analysis of patterns revealed that bands identified as Atopobium vaginae were present in a majority (12/22) of BV positive patients, while corresponding bands were rare (2/24) in normal patients. (P < 0.001) Two A. vaginae isolates were cultivated from two patients whose DGGE analyses indicated the presence of this organism. Two A. vaginae 16S rRNA gene sequences were identified among the clinical isolates. The same two sequences were obtained from DGGE bands of the corresponding vaginal flora. The sequences differed by one nucleotide over the short (~300 bp) segment used for DGGE analysis and migrated to slightly different points in denaturing gradient gels. Both isolates were strict anaerobes and highly metronidazole resistant. CONCLUSION: The results suggest that A. vaginae may be an important component of the complex bacterial ecology that constitutes abnormal vaginal flora. This organism could play a role in treatment failure if further studies confirm it is consistently metronidozole resistant

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications