AIMC Topic: Models, Statistical

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Supporting equitable and responsible highway safety improvement funding allocation strategies - Why AI prediction biases matter.

Accident; analysis and prevention
The existing methodologies for allocating highway safety improvement funding closely rely on the utilization of crash prediction models. Specifically, these models produce predictions that estimate future crash hazard levels in different geographical...

PERform: assessing model performance with predictivity and explainability readiness formula.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis
In the rapidly evolving field of artificial intelligence (AI), explainability has been traditionally assessed in a post-modeling process and is often subjective. In contrary, many quantitative metrics have been routinely used to assess a model's perf...

A neural network paradigm for modeling psychometric data and estimating IRT model parameters: Cross estimation network.

Behavior research methods
This paper presents a novel approach known as the cross estimation network (CEN) for fitting the datasets obtained from psychological or educational tests and estimating the parameters of item response theory (IRT) models. The CEN is comprised of two...

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Journal of environmental management
Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites...

Computational frameworks integrating deep learning and statistical models in mining multimodal omics data.

Journal of biomedical informatics
BACKGROUND: In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing ...

Model-agnostic explanations for survival prediction models.

Statistics in medicine
Advanced machine learning methods capable of capturing complex and nonlinear relationships can be used in biomedical research to accurately predict time-to-event outcomes. However, these methods have been criticized as "black boxes" that are not inte...

A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.

Statistical methods in medical research
Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history....

What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing.

Pharmaceutical statistics
Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose o...

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

PloS one
BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series appr...

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

PloS one
Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to t...