AIMC Topic: Models, Statistical

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Making Group Decisions within the Framework of a Probabilistic Hesitant Fuzzy Linear Regression Model.

Sensors (Basel, Switzerland)
A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when work...

Volatility forecasts of stock index futures in China and the US-A hybrid LSTM approach.

PloS one
This paper is concerned with the unsolved issue of how to accurately predict the financial market volatility. We propose a novel volatility prediction method for stock index futures prediction based on LSTM, PCA, stock indices and relevant futures. I...

Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure.

International journal of cardiology
BACKGROUND: Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and...

A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation.

Kidney360
BACKGROUND: The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decisio...

Mediation analysis using Bayesian tree ensembles.

Psychological methods
We present a general framework for causal mediation analysis using nonparametric Bayesian methods in the potential outcomes framework. Our model, which we refer to as the Bayesian causal mediation forests model, combines recent advances in Bayesian m...

Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities.

Journal of biomedical informatics
Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed eva...

A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands.

International journal of health geographics
BACKGROUND: Local policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being o...

Sum-Product Networks: A Survey.

IEEE transactions on pattern analysis and machine intelligence
A sum-product network (SPN) is a probabilistic model, based on a rooted acyclic directed graph, in which terminal nodes represent probability distributions and non-terminal nodes represent convex sums (weighted averages) and products of probability d...

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.

Frontiers in public health
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed ...

Heavyweight Statistical Alignment to Guide Neural Translation.

Computational intelligence and neuroscience
Transformer neural models with multihead attentions outperform all existing translation models. Nevertheless, some features of traditional statistical models, such as prior alignment between source and target words, prove useful in training the state...