PSO-BiLSTM-Attention: An Interpretable Deep Learning Model Optimized by Particle Swarm Optimization for Accurate Ischemic Heart Disease Incidence Forecasting.

Ruihang Zhang, Shiyao Wang, Wei Sun, Yanming Huo

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ruihang ZhangGraduate School, China Academy of Chinese Medical Sciences, Beijing 100102, China.

Shiyao WangDepartment of Agrotechnology and Food Sciences, Wageningen University and Research, 6708 PB Wageningen, The Netherlands.

Wei SunGraduate School, China Academy of Chinese Medical Sciences, Beijing 100102, China.ORCID 0009-0003-7975-7873

Yanming HuoGraduate School, China Academy of Chinese Medical Sciences, Beijing 100102, China.

Funding

the Science and Technology Innovation Project of China Academy of Chinese Medical Sciences CI2021A00909the Self-selected project of China Academy of Chinese Medical Sciences WJYY-ZZXT-2023-11

6 · The paper itself

Abstract

Ischemic heart disease (IHD) remains the predominant cause of global mortality, necessitating accurate incidence forecasting for effective prevention strategies. Existing statistical models inadequately capture nonlinear epidemiological patterns, while deep learning approaches lack clinical interpretability. We constructed an interpretable predictive framework combining particle swarm optimization (PSO), bidirectional long short-term memory (BiLSTM) networks, and a novel multi-scale attention mechanism. Age-standardized incidence rates (ASIRs) from the Global Burden of Disease (GBD) 2021 database (1990-2021) were stratified across 24 sex-age subgroups and processed through 10-year sliding windows with advanced feature engineering. SHapley Additive exPlanations (SHAP) provided a three-level interpretability analysis (global, local, and component). The framework achieved superior performance metrics: mean absolute error (MAE) of 0.0164, root mean squared error (RMSE) of 0.0206, and R

Indexed as

attention mechanismBidirectional Long Short-Term Memory networksdeep learningincidence predictioninterpretabilityischemic heart diseaseparticle swarm optimization

Identifiers

PMID41463640

DOI10.3390/bioengineering12121343

PMCPMC12729313

What Socratic holds

Textmetadata

LicenceCC BY

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Registered trials

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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 Socratic graph.