The future of evaluation of child and adolescent psychiatric treatments

Falissard, Bruno

doi:10.14744/iacapaparxiv.2020.20007

Cited by 100 publications

(153 citation statements)

References 100 publications

(161 reference statements)

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“…Clustering methods, such as k‐means clustering, 46 and neural network, 47 evaluate repeated measure using Euclidean distance or computational models. In this kind of models, similarity is determined by how well the responses fit the same shape of curve, often requiring large data sets.…”

Section: Discussionmentioning

confidence: 99%

Clustering ultrasonic waves propagation time: A hierarchical polynomial semiparametric approach

Zuanetti

Paz

Rodrigues

et al. 2021

Appl Stoch Models Bus & Ind

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In the field of civil engineering, nondestructive evaluation (NDE) can be understood as the process of a material or structural element assessment based on techniques that do not introduces damage to the assessed sample. NDE can be used to inspect the quality of materials or to find some damages, as cracks and voids. This way, ultrasonic data monitoring has become one of the most important tools for detecting local damages in civil engineering elements. Concerning to masonry application, this method consists in the measurement of the time that the ultrasonic wave takes to pass through a material and changes in the ultrasonic wave propagation can be used for identifying damages presence and anomaly. Despite statistical methods be widely used for processing large data sets, in the case of ultrasonic waves some issues need to be overcoming, as the analysis of the path traveled by the ultrasonic wave. Thus, the main aim of this work is to analyze the ultrasonic wave propagation behavior through a new approach of a statistical model. Using a Bayesian semiparametric method with Dirichlet process, a data‐driven hierarchical regression model is applied to simulated and experimental data sets. For simulated data sets, the sensibility of the model in clustering profiles in an unknown number of groups and also detecting outliers is observed. The application in experimental data shows the potential use of the model for real case applications due to its efficiency in identifying different behaviors of the wave propagation in materials. Therefore, considering the advanced of automation and Internet of Things, statistical methods based on clustering are important and useful tools for being explored in the masonry conditions monitoring.

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Section: Discussionmentioning

confidence: 99%

Clustering ultrasonic waves propagation time: A hierarchical polynomial semiparametric approach

Zuanetti

Paz

Rodrigues

et al. 2021

Appl Stoch Models Bus & Ind

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“…Both panels illustrate results based on the theoretical transition data computed in the present work. The left panel also includes results based on the experimental data of Musielok et al (1995) and Bridges & Wiese (2010), while the right panel includes results based on the theoretical data of Tachiev & Froese Fischer (2002), Hibbert et al (1991), andBautista et al (2022). Horizontal lines show the mean abundance inferred from each data set.…”

Section: Validation Via the Solar Spectrummentioning

confidence: 99%

“…Among these theoretical results, the MCHF-BP values (Tachiev & Froese Fischer 2002;Froese Fischer & Tachiev 2004) are in overall better agreement with experimental results than the others. More recently, Bautista et al (2022) calculated the gf -values of the two lines at 8683 Å and 8629 Å, which are diagnostics of the solar nitrogen abundance, using a combination of different methods, i.e. AUTOSTRUCTURE based on the Thomas-Fermi-Dirac-Amaldi central potential, pseudo-relativistic Hartree-Fock, and multiconfiguration Dirac-Hartree-Fock (MCDHF) methods.…”

Section: Introductionmentioning

confidence: 99%

Extended MCDHF calculations of energy levels and transition data for N I

Meichun¹,

Li²,

Jönsson³

et al. 2023

Preprint

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Accurate and extensive atomic data are essential for spectroscopic analyses of stellar atmospheres and other astronomical objects. We present energy levels, lifetimes, and transition probabilities for neutral nitrogen, the sixth most abundant element in the cosmos. The calculations employ the fully relativistic multiconfiguration Dirac-Hartree-Fock and relativistic configuration interaction methods, and span the 103 lowest states up to and including 2s 2 2p 2 5s. Our theoretical energies are in excellent agreement with the experimental data, with an average relative difference of 0.07%. In addition, our transition probabilities are in good agreement with available experimental and theoretical data. We further verify the agreement of our data with experimental results via a re-analysis of the solar nitrogen abundance, with the results from the Babushkin and Coulomb gauges consistent to 2% or 0.01 dex. We estimated the uncertainties of the computed transition data based on a statistical analysis of the differences between the transition rates in Babushkin and Coulomb gauges. Out of the 1701 computed electric dipole transitions in this work, 83 (536) are associated with uncertainties less than 5% (10%).

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“…Authors in [33] used neural networks, which take advantage of few-shot learning and autoencoder to perform predictive analysis on COVID-19 data. Some studies also focus on finding the conditional dependencies between features, which can be used to analyze the behavior of different features towards the prediction of label [34]. A study for predicting the clinical outcomes of patients and indicating whether patients are more likely to recover from coronavirus or in danger of death is performed in [4].…”

Section: Related Workmentioning

confidence: 99%

Efficient analysis of COVID-19 clinical data using machine learning models

Ali

Zhou

Patterson

2022

Med Biol Eng Comput

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Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before by leveraging such big data. This data is of many different varieties and can be of different levels of veracity, e.g., precise, imprecise, uncertain, and missing, making it challenging to extract meaningful information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -often in real-time -the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning-based algorithms to this big data is a natural approach to take to this aim since they can quickly scale to such data and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19 and potential future pandemics in general. In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation and then propose a model that first performs efficient feature selection from such representation. We apply this approach to two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most cases. We also computed the importance of different attributes in the dataset using information gain. This can help the policymakers focus on only certain attributes to study this disease rather than focusing on multiple random factors that may not be very informative to patient outcomes.

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The future of evaluation of child and adolescent psychiatric treatments

Cited by 100 publications

References 100 publications

Clustering ultrasonic waves propagation time: A hierarchical polynomial semiparametric approach

Clustering ultrasonic waves propagation time: A hierarchical polynomial semiparametric approach

Extended MCDHF calculations of energy levels and transition data for N I

Efficient analysis of COVID-19 clinical data using machine learning models

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