ArticleScientific reports2025
Artificial neural network-driven approaches to improved forecasting of disability care expenditures in an aging Kingdom of Saudi Arabia population.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Estimating long-term care needs in data-scarce settings: a diagnostic model with evidence from MENA.Population health metrics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The total number of older persons globally (those aged 60 years and above) was 202 million in 1950; this total multiplied to attain 901 million and is predicted to triple again in 2100. The growth percentage of the elderly population is quickly improving, and the value of their care shall pose a challenging problem in the future. Notably, the number of older persons in the Kingdom of Saudi Arabia (KSA) is fast growing, from 5% of the entire population in 2015 to a predicted 20.9% by 2050. The main problem is the KSA's management of the rising problem of age-related Non-Communicable Diseases (NCD). With the escalating dimensions of the population of older persons and increased prevalence of NCD causes of risk, the occurrence of NCDs in KSA will rise, resulting in a proportional increase in the requirement for medical assistance. In this paper, an Artificial Neural Network-Based Approaches for Improved Forecasting of Disability Care Expenditures in an Aging Kingdom of Saudi Arabia Population (ANNFDCE-AKSAP) method is proposed. The main objective of the ANNFDCE-AKSAP method is to create an accurate and scalable forecasting system capable of addressing the Kingdom's evolving disability care needs. Initially, the ANNFDCE-AKSAP technique utilizes a min-max normalization-based data preprocessing model to ensure consistent scaling across variables. Furthermore, the bidirectional variational autoencoder with the self-attention module (BiVAE-SAM) model forecasts disability care expenses. Finally, the enriched coati osprey algorithm (ECOA)-based hyperparameter selection process is performed to optimize the prediction results of the BiVAE-SAM method. A wide range of simulations is accomplished to demonstrate the enhanced performance of the ANNFDCE-AKSAP technique, and the results are inspected using several measures. The comparison study of the ANNFDCE-AKSAP technique illustrated the lowest MSE, 0.0128, and MAE, 0.0942, compared to all other methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.