84,027 research outputs found
Bayesian leave-one-out cross-validation for large data
Model inference, such as model comparison, model checking, and model
selection, is an important part of model development. Leave-one-out
cross-validation (LOO) is a general approach for assessing the generalizability
of a model, but unfortunately, LOO does not scale well to large datasets. We
propose a combination of using approximate inference techniques and
probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation
for large datasets. We provide both theoretical and empirical results showing
good properties for large data.Comment: Accepted to ICML 2019. This version is the submitted pape
Utility of the International Classification of Functioning, Disability and Health (ICF) for educational psychologists’ work
Despite embracing a bio-psycho-social perspective, the World Health Organization’s International Classification of Functioning, Disability and Health (ICF) assessment framework has had limited application to date with children who have special educational needs (SEN). This study examines its utility for educational psychologists’ work with children who have Autism Spectrum Disorders (ASD). Mothers of 40 children with ASD aged eight to 12 years were interviewed using a structured protocol based on the ICF framework. The Diagnostic Interview for Social and Communication Disorder (DISCO) was completed with a subset of 19 mothers. Internal consistency and inter-rater reliability of the interview assessments were found to be acceptable and there was evidence for concurrent and discriminant validity. Despite some limitations, initial support for the utility of the ICF model suggests its potential value across educational, health and care fields. Further consideration of its relevance to educational psychologists in new areas of multi-agency working is warranted
Predictivity of clinical, laboratory and imaging findings in diagnostic definition of palpable thyroid nodules. A multicenter prospective study
Abstract
PURPOSE:
To assess the role of clinical, biochemical, and morphological parameters, as added to cytology, for improving pre-surgical diagnosis of palpable thyroid nodules.
METHODS:
Patients with a palpable thyroid nodule were eligible if surgical intervention was indicated after a positive or suspicious for malignancy FNAC (TIR 4-5 according to the 2007 Italian SIAPEC-IAP classification), or two inconclusive FNAC at a 653 months interval, or a negative FNAC associated with one or more risk factor. Reference standard was histological malignancy diagnosis. Likelihood ratios of malignancy, sensitivity, specificity, negative (NPV), and positive predictive value (PPV) were described. Multiple correspondence analysis (MCA) and logistic regression were applied.
RESULTS:
Cancer was found in 433/902 (48%) patients. Considering TIR4-5 only as positive cytology, specificity, and PPV were high (94 and 91%) but sensitivity and NPV were low (61 and 72%); conversely, including TIR3 among positive, sensitivity and NPV were higher (88 and 82%) while specificity and PPV decreased (52 and 63%). Ultrasonographic size 653\u2009cm was independently associated with benignity among TIR2 cases (OR of malignancy 0.37, 95% CI 0.18-0.78). In TIR3 cases the hard consistency of small nodules was associated with malignity (OR: 3.51, 95% CI 1.84-6.70, p\u2009<\u20090.001), while size alone, irrespective of consistency, was not diagnostically informative. No other significant association was found in TIR2 and TIR3.
CONCLUSIONS:
The combination of cytology with clinical and ultrasonographic parameters may improve diagnostic definition of palpable thyroid nodules. However, the need for innovative diagnostic tools is still high
Benchmarking Treatment Response in Tourette’s Disorder: A Psychometric Evaluation and Signal Detection Analysis of the Parent Tic Questionnaire
This study assessed the psychometric properties of a parent-reported tic severity measure, the Parent Tic Questionnaire (PTQ), and used the scale to establish guidelines for delineating clinically significant tic treatment response. Participants were 126 children ages 9 to 17 who participated in a randomized controlled trial of Comprehensive Behavioral Intervention for Tics (CBIT). Tic severity was assessed using the Yale Global Tic Severity Scale (YGTSS), Hopkins Motor/Vocal Tic Scale (HMVTS) and PTQ; positive treatment response was defined by a score of 1 (very much improved) or 2 (much improved) on the Clinical Global Impressions – Improvement (CGI-I) scale. Cronbach’s alpha and intraclass correlations (ICC) assessed internal consistency and test-retest reliability, with correlations evaluating validity. Receiver- and Quality-Receiver Operating Characteristic analyses assessed the efficiency of percent and raw-reduction cutoffs associated with positive treatment response. The PTQ demonstrated good internal consistency (α = 0.80 to 0.86), excellent test-retest reliability (ICC = .84 to .89), good convergent validity with the YGTSS and HM/VTS, and good discriminant validity from hyperactive, obsessive-compulsive, and externalizing (i.e., aggression and rule-breaking) symptoms. A 55% reduction and 10-point decrease in PTQ Total score were optimal for defining positive treatment response. Findings help standardize tic assessment and provide clinicians with greater clarity in determining clinically meaningful tic symptom change during treatment
Mining Frequent Graph Patterns with Differential Privacy
Discovering frequent graph patterns in a graph database offers valuable
information in a variety of applications. However, if the graph dataset
contains sensitive data of individuals such as mobile phone-call graphs and
web-click graphs, releasing discovered frequent patterns may present a threat
to the privacy of individuals. {\em Differential privacy} has recently emerged
as the {\em de facto} standard for private data analysis due to its provable
privacy guarantee. In this paper we propose the first differentially private
algorithm for mining frequent graph patterns.
We first show that previous techniques on differentially private discovery of
frequent {\em itemsets} cannot apply in mining frequent graph patterns due to
the inherent complexity of handling structural information in graphs. We then
address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling
based algorithm. Unlike previous work on frequent itemset mining, our
techniques do not rely on the output of a non-private mining algorithm.
Instead, we observe that both frequent graph pattern mining and the guarantee
of differential privacy can be unified into an MCMC sampling framework. In
addition, we establish the privacy and utility guarantee of our algorithm and
propose an efficient neighboring pattern counting technique as well.
Experimental results show that the proposed algorithm is able to output
frequent patterns with good precision
Mining whole sample mass spectrometry proteomics data for biomarkers: an overview
In this paper we aim to provide a concise overview of designing and conducting an MS proteomics experiment in such a way as to allow statistical analysis that may lead to the discovery of novel biomarkers. We provide a summary of the various stages that make up such an experiment, highlighting the need for experimental goals to be decided upon in advance. We discuss issues in experimental design at the sample collection stage, and good practise for standardising protocols within the proteomics laboratory. We then describe approaches to the data mining stage of the experiment, including the processing steps that transform a raw mass spectrum into a useable form. We propose a permutation-based procedure for determining the significance of reported error rates. Finally, because of its general advantages in speed and cost, we suggest that MS proteomics may be a good candidate for an early primary screening approach to disease diagnosis, identifying areas of risk and making referrals for more specific tests without necessarily making a diagnosis in its own right. Our discussion is illustrated with examples drawn from experiments on bovine blood serum conducted in the Centre for Proteomic Research (CPR) at Southampton University
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