Results 31 to 40 of about 40,132 (204)

Clinical Model‐Informed Precision Dosing Consult Service for Accelerating Personalized Medication in Pediatric Patients

Clinical Pharmacology &Therapeutics, EarlyView.
Traditional dosing strategies often rely on a “one‐size‐fits‐all” paradigm, assuming an “average” patient with typical demographic and pharmacological characteristics. In reality, this often overlooks existing between‐patient variability and can lead to suboptimal drug exposure or toxicity. This issue is especially pronounced in pediatric patients, who
Zachary L. Taylor, Takanobu Nadai, Kei Irie, Wen Rui Tan, Sanjana Parikh, Anhar Hosawi, Matthijs W. van Hoogdalem, Ronaldo Morales Junior, Sonya Tang Girdwood, Abbie D. Leino, Kana Mizuno, Alexander A. Vinks, Tomoyuki Mizuno   +12 more
wiley   +1 more source

Quantum Bayes’ Theorem

, 2013
An extension of Bayes’ theorem into the quantum ...
Haken, Hermann
core   +1 more source

Probability prediction of true‐triaxial compressive strength of intact rocks based on the improved PSO‐RVM model

Deep Underground Science and Engineering, EarlyView.
In this work, we propose an improved particle swarm optimization (PSO) algorithm and develop an improved PSO‐relevance vector machine (RVM) model as a substitute for traditional true‐triaxial testing. The model's high prediction accuracy was validated through comparisons with two other machine learning methods and five three‐dimensional Hoek–Brown type
Qi Zhang, Maohui Wang, Ning Wang, Yixin Shen, Xuzhen He   +4 more
wiley   +1 more source

Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges

Epilepsia Open, EarlyView.
Abstract Preclinical translational epilepsy research uses animal models to better understand the mechanisms underlying epilepsy and its comorbidities, as well as to analyze and develop potential treatments that may mitigate this neurological disorder and its associated conditions. Artificial intelligence (AI) has emerged as a transformative tool across
Jesús Servando Medel‐Matus, Cesar Santana‐Gomez, Ruby G. Escalante, Dominique Duncan, Pedro F. Viana, Giulia Sofia Cereda, Naoto Kuroda, Aristea S. Galanopoulou   +7 more
wiley   +1 more source

Creation of a Landslide Susceptibility Map Using Short‐Term Data From the July 2018 Heavy Rainfall in Southern Hiroshima Prefecture

Geological Journal, EarlyView.
This work advances landslide susceptibility mapping by incorporating short‐term trigger data with landscape susceptibility mapping. We also examine the importance of downsampling, watershed delineation and geospatial correlations in evaluating outcomes.
Kanta Kotsugi, Ting Liu, Toshiro Hata, Rebecca Muenich   +3 more
wiley   +1 more source

Bayes’ Theorem in Mammography

, 1970
Bayes’ theorem is a useful mathematical and statistical tool for calculating the probability of a given diagnosis from the clinical signs and their incidence in the various diagnoses considered. The theorem is highly pertinent to mammography, especially
Francesco Di Pietrantonj, Ugo Salvolini
core   +1 more source

Robust Tests of Forecast Accuracy for Factor‐Augmented Regressions With an Application to the Novel EA‐MD‐QD Dataset

Journal of Applied Econometrics, EarlyView.
ABSTRACT We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors,
Alessandro Morico, Ovidijus Stauskas
wiley   +1 more source

Bayes' theorem in palaeopathological diagnosis

, 2003
The utility of Bayes' theorem in paleopathological diagnoses is explored. Since this theorem has been used heavily by modern clinical medicine, its usefulness in that field is described first.
Byers, S., Roberts, C.A.
core   +1 more source

Bayesian Model Averaging in Causal Instrumental Variable Models

Journal of Applied Econometrics, EarlyView.
ABSTRACT Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian model averaging procedure that addresses this challenge by averaging across different sets of instrumental variables and covariates in a structural equation ...
Gregor Steiner, Mark Steel
wiley   +1 more source

Wavelength-Resolution SAR Change Detection Using Bayes' Theorem

, 2020
This article presents Bayes' theorem for wavelength-resolution synthetic aperture radar (SAR) change detection method development. Different change detection methods can be derived using Bayes' theorem in combination with the target model, clutter-plus ...
Palm, Bruna Gregory,, Machado, Renato, Hellsten, Hans, Uchoa-Filho, Bartolomeu F.,, Pettersson, Mats, Dammert, Patrik, Dammert, Patrik,, Hellsten, Hans,, Uchoa-Filho, Bartolomeu F., Machado, Renato,, Palm, Bruna Gregory, Vu, Viet Thuy,, Alves, Dimas Irion, Pettersson, Mats,, Vu, Viet Thuy, Alves, Dimas Irion,   +15 more
core   +1 more source