AIMC Topic: Models, Statistical

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A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy.

Physics in medicine and biology
. In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical ...

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator.

Ultrasonics
The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite compli...

Putting the Personalized Metabolic Avatar into Production: A Comparison between Deep-Learning and Statistical Models for Weight Prediction.

Nutrients
Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent s...

Statistical models versus machine learning for competing risks: development and validation of prognostic models.

BMC medical research methodology
BACKGROUND: In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growi...

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.

Frontiers in public health
INTRODUCTION: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to he...

Forecasting the United State Dollar(USD)/Bangladeshi Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange rates.

PloS one
Forecasting a currency exchange rate is one of the most challenging tasks nowadays. Due to government monetary policy and some uncertain factors, such as political stability, it becomes difficult to correctly forecast the currency exchange rate. Prev...

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches.

Journal of exposure science & environmental epidemiology
BACKGROUND: Disparities in adverse COVID-19 health outcomes have been associated with multiple social and environmental stressors. However, research is needed to evaluate the consistency and efficiency of methods for studying these associations at lo...

Long-term care insurance purchase decisions of registered nurses: Deep learning versus logistic regression models.

Health policy (Amsterdam, Netherlands)
OBJECTIVE: The purpose of this study was to use a deep learning model and a traditional statistical regression model to predict the long-term care insurance decisions of registered nurses.

Comparison of Artificial Neural Network and Polynomial Approximation Models for Reflectance Spectra Reconstruction.

Sensors (Basel, Switzerland)
Knowledge of surface reflection of an object is essential in many technological fields, including graphics and cultural heritage. Compared to direct multi- or hyper-spectral capturing approaches, commercial RGB cameras allow for a high resolution and...

Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design.

Scientific reports
Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data ...