400 hasil
Menampilkan 1–20 · 0,64 detik · RSS
Healthcare Analytics
Scopus
ISSN 2772-4425 · Elsevier · 400 artikel di indeks
-
Artikel Jurnal
An analytical evaluation of imputation methods for enhancing cardiac care data integrity
Electronic Health Records (EHRs) are comprised of digitally stored patient and population health data. Unfortunately, EHRs are often far from complete, and these incomplete health records are referred to as missingness. Missingness in EHRs is a hindrance factor in utilizing Machine Learning (ML)…
-
Artikel Jurnal
An analytical modeling framework for breast cancer progression and treatment evaluation
This paper presents a mathematical model of breast cancer composed of six compartments: one representing tumor cells, two representing cytokine populations, and three representing immune cell types. The proposed framework is original in that it integrates cytokine-mediated (IL-2 and IFN-γ) feedback…
-
Artikel Jurnal
An interpretable machine learning approach to predicting depression and diabetes
This study develops and compares interpretable machine learning (ML) models that independently predict the risk of diabetes and depression using a dataset from the Behavioral Risk Factor Surveillance System (BRFSS) for individuals aged 18 to 64 from 2016 to 2022. By incorporating behavioral…
-
Artikel Jurnal
A hierarchical Bayesian approach for predictive analytics of depression severity in medical students
Medical students experience disproportionately high rates of depression due to intense academic pressures and clinical demands. Without timely, targeted intervention, they face increased risks of academic underperformance and adverse outcomes. Existing predictive models often adopt a…
-
Artikel Jurnal
An advanced deep learning approach for medical image analytics in brain tumor diagnosis
Brain tumors remain difficult to detect at an early stage, and reliable detection on magnetic resonance imaging (MRI) scans is hindered by dataset variability, subtle inter-class similarities, and the limited generalization of existing deep learning models. Despite recent advances in deep…
-
Artikel Jurnal
An unsupervised learning approach to optimising tumour therapy through clinical data mining
Clinical data mining and healthcare analytics enable systematic evaluation of treatment strategies in precision oncology. This study analysed a harmonised multi-centre dataset of 150 patients treated with linac-based stereotactic radiosurgery for vestibular schwannoma across three institutions in…
-
Artikel Jurnal
An explainable adaptive graph learning framework for dynamic brain connectivity analysis in autism diagnosis
Autism Spectrum Disorder (ASD) is increasingly understood through the lens of large-scale brain connectivity, yet most computational approaches fail to capture the dynamic, subject-specific neural interactions that define it. Recent graph neural network (GNN) methods have incorporated adaptive…
-
Artikel Jurnal
A continuous-time predictive analytics framework for quantifying information degradation in intensive care monitoring systems
Continuous-time intensive care monitoring requires predictive analytics systems to infer physiologic states from data streams that are inherently irregular and workflow-driven. Current clinical AI pipelines often overlook how model uncertainty accumulates between observations, creating a risk of…
-
Artikel Jurnal
A prescriptive analytics framework for product sequencing in pharmaceutical packaging lines
Pharmaceutical manufacturing exhibits a challenge that must be addressed due to the evolving demands of its products and the rapid response required by the industry. Operational efficiency in pharmaceutical packaging is often hindered by costly setup times and manual scheduling limitations. This…
-
Artikel Jurnal
A densely connected deep learning method for joint brain tumor segmentation and classification
Accurate brain tumor analysis from magnetic resonance imaging (MRI) requires both tumor localization and tumor-type classification. Existing pipelines often treat segmentation and classification as isolated or sequential tasks, limiting shared representation learning and potentially introducing…
-
Artikel Jurnal
Hybrid computational approach, modeling and stability analysis of fractional order Huntington’s disease model
Huntington’s disease (HD) is a fatal genetic ailment that causes the brain’s nerve cells to gradually deteriorate, limiting mood, thinking, and movement. There is now no cure for this inherited condition, which worsens with time. We employed a set of fractional-order differential equations to…
-
Artikel Jurnal
An explainable spatio-temporal graph neural network for seizure localization using multi-domain analytics
Detecting epileptic seizures from scalp electroencephalographic (EEG) is made difficult by the nonstationary nature of brain signals and the complex spatio-temporal interactions between neural regions in different locations. This paper introduces a spatio-temporal channel-attention graph neural…
-
Artikel Jurnal
A data-driven approach to depression detection and assessment using minimal brain signal sensing
Major depressive disorder (MDD) remains one of the most prevalent mental health conditions worldwide, yet its diagnosis continues to rely mainly on subjective clinical assessment. Electroencephalography (EEG) provides an objective and noninvasive means of capturing neural correlates of depression…
-
Artikel Jurnal
A contrastive and uncertainty-aware analytics framework for reliable lung disease detection from chest imaging
The diagnosis of lung diseases from chest X-ray (CXR) images remains challenging due to inter-observer variability among radiologists and delays in clinical decision-making. Although Vision Transformer (ViT)-based architectures have shown strong performance in medical image classification, their…
-
Artikel Jurnal
A geospatial analytics framework for assessing pharmacy service environments
Pharmacies are a critical component of healthcare delivery in the Philippines, yet their spatial distribution remains uneven and misaligned with population demand. This study investigates the geography of pharmacy access by introducing a facility-level urbanicity index based on the density of…
-
Artikel Jurnal
A penalty-embedded genetic algorithm for interpretable clinical feature selection in heart disease prediction
Heart disease decision support often requires identifying a small set of routine clinical tests that preserves reliable multi-class predictive performance while reducing diagnostic burden. However, many metaheuristic wrapper studies emphasize accuracy and rely on implicit or post hoc size control…
-
Artikel Jurnal
An analytics-driven optimization framework for nurse scheduling under uncertainty
Nurse scheduling in hospitals is a complex optimization problem influenced by uncertain staff absences, regulatory constraints, and the need to ensure fair workload distribution. Traditional scheduling models often overlook the unpredictability of last-minute absences, leading to operational…
-
Artikel Jurnal
A data-centric approach for optimizing multi-objective healthcare service accessibility and equity
The optimal location of healthcare facilities is a critical challenge in public health, directly influencing service accessibility and efficiency. This study presents a data-driven optimization framework for healthcare facility location, leveraging real-world data from three Brazilian cities within…
-
Artikel Jurnal
An automated analytics approach for diabetic retinopathy detection with ensemble deep learning models in healthcare
Diabetic Retinopathy (DR) is a leading complication of prolonged diabetes, which poses a significant threat to vision and may lead to permanent blindness. Early identification and timely intervention are crucial to preventing disease progression. Traditionally, DR diagnosis relies on medical…
-
Artikel Jurnal
An analytics framework for graph-based anomaly detection in healthcare time series
Anomaly detection in time series plays a vital role in diverse domains such as healthcare, finance, and industrial monitoring, where identifying deviations from normal behavior can signal critical events. While traditional methods often focus on univariate time series and assume fixed temporal…