AIMC Topic: Models, Statistical

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Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.

The Science of the total environment
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...

An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.

BMC medical informatics and decision making
BACKGROUND: Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. This research aimed to develop an explain...

TransformerLSR: Attentive joint model of longitudinal data, survival, and recurrent events with concurrent latent structure.

Artificial intelligence in medicine
In applications such as biomedical studies, epidemiology, and social sciences, recurrent events often co-occur with longitudinal measurements and a terminal event, such as death. Therefore, jointly modeling longitudinal measurements, recurrent events...

A supervised machine learning statistical design of experiment approach to modeling the barriers to effective snakebite treatment in Ghana.

PLoS neglected tropical diseases
BACKGROUND: Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths an...

Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.

Journal of pharmacokinetics and pharmacodynamics
In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Tre...

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

The British journal of mathematical and statistical psychology
Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) ...

Modelling the effect of base component properties and processing conditions on mixture products using probabilistic, knowledge-guided neural networks.

International journal of pharmaceutics
Development of materials by mixing different base components is a widespread methodology to create materials with improved properties compared to those of its base components. However, efficient determination of the properties of mixture-based materi...

Predictive modelling of linear growth faltering among pediatric patients with Diarrhea in Rural Western Kenya: an explainable machine learning approach.

BMC medical informatics and decision making
INTRODUCTION: Stunting affects one-fifth of children globally with diarrhea accounting for an estimated 13.5% of stunting. Identifying risk factors for its precursor, linear growth faltering (LGF), is critical to designing interventions. Moreover, de...

Predicting the time to get back to work using statistical models and machine learning approaches.

BMC medical research methodology
BACKGROUND: Whether machine learning approaches are superior to classical statistical models for survival analyses, especially in the case of lack of proportionality, is unknown.

Machine learning in causal inference for epidemiology.

European journal of epidemiology
In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimate...