Technology and Health Care最新文献

{"title":"Epileptic seizure onset early warning using a lightweight multimodal self-explainable neural network.","authors":"Daraje Kaba Gurmessa, Kula Kekeba Tune, Worku Jimma","doi":"10.1177/09287329261498672","DOIUrl":"https://doi.org/10.1177/09287329261498672","url":null,"abstract":"<p><p>BackgroundCutting-edge deep learning algorithms are applied to epileptic seizures to uncover hidden patterns and achieve high accuracy. Post hoc algorithms explain black-box models but do not reveal the detailed workings of the algorithms. Therefore, intrinsic methods overcome these issues; however, they suffer from improper use of indicative metrics and performance-to-accuracy trade-offs.ObjectiveThis study implements a lightweight multimodal explainable Neural Network for real-time early warning of epileptic seizure onset.MethodsPeople with drug-resistant epilepsy across all age groups who had regular follow-up at Emanuel Mental Specialized Hospital in Ethiopia during the data collection period were the study population. EEG recordings were performed using the 10/20 system. A pretest was conducted two weeks before actual data collection. EDFbrowser was used for preparing the dataset in CSV format. Learned and identity self-explainable neural network algorithms were used to develop a model. In addition to classical evaluation metrics, explicitness, faithfulness, stability, and clear patterns behind predictions were used. Ethical approval was also obtained from the institutional review board (IRB) with reference number No: AM/146/2/36.ResultsLearned and identity self-explainable neural networks were developed and compared. The models' accuracies are 96.524% and 95.328%, and their stability mean values are 99.88% and 99.88%, respectively. Additionally, explicitness values of 0.0055 and 0.01197 were achieved, respectively.ConclusionThe results demonstrate that using a self-explainable neural network is a promising step toward responsible and transparent artificial intelligence. Comparing the two models shows that the learned self-explainable neural network is slightly more optimistic than the identity self-explainable neural network.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261498672"},"PeriodicalIF":1.3,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensor-integrated AI-assisted gait training in dementia and Parkinson's disease: A single-session pre-post study.","authors":"Chien-Tai Hong, Li-Kai Huang, Cheng-Hsing Liu, Jia-Hung Chen, Lung Chan","doi":"10.1177/09287329261498488","DOIUrl":"https://doi.org/10.1177/09287329261498488","url":null,"abstract":"<p><p>BackgroundGait impairment in dementia and Parkinson's disease (PD) contributes to falls, reduced mobility, and loss of independence. Sensor-integrated, AI-assisted gait training systems may provide standardized rehabilitation with real-time feedback and quantitative gait assessment.<b>Objective:</b> To investigate whether a single session of AI-assisted gait training improves spatiotemporal gait parameters in individuals with dementia and PD.MethodsThis single-session pre-post study enrolled ambulatory adults aged 60-85 years with dementia or PD. Participants completed one gait-training session using the Best Shape Gait (BSG) platform, with a planned duration of 15-30 min and the actual duration adjusted according to individual physical tolerance. Primary outcome was gait-speed change. Secondary outcomes included step length, step count, and gait variability. Paired t tests were performed within four predefined subgroups: Dementia-Support (n = 67), Dementia-Escort (n = 43), PD-Support (n = 64), and PD-Escort (n = 43).ResultsGait speed significantly increased after training across all predefined subgroups. In Support-mode walking, both dementia and PD cohorts demonstrated reduced step count and increased step length, suggesting improved locomotor efficiency. In Escort-mode walking, gait speed increased and step count decreased significantly in both cohorts, whereas changes in step length and gait variability were not significant. Gait variability did not significantly change in either cohort. Gait-speed improvement was consistent across dementia subtypes, modified Hoehn and Yahr stages, age groups, and fall-history categories. Baseline MMSE score and levodopa equivalent daily dose were not significantly associated with gait-speed improvement.ConclusionsA single session of sensor-integrated, AI-assisted gait training was associated with immediate within-session improvements in gait speed in individuals with dementia and PD. These findings support the feasibility of standardized, minimally supervised gait training in geriatric neurorehabilitation settings.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261498488"},"PeriodicalIF":1.3,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A wearable vibroarthrography system with PID-controlled negative-pressure suction and synchronized posture estimation.","authors":"Jiun-Hung Lin, Shih-Tsang Tang","doi":"10.1177/09287329261496167","DOIUrl":"https://doi.org/10.1177/09287329261496167","url":null,"abstract":"<p><p>BackgroundVibroarthrography (VAG) is a non-invasive technique for evaluating joint health through acoustic signals generated during movement. However, reliable signal acquisition during dynamic activities remains challenging due to sensor displacement, motion artifacts, and inconsistent skin contact.ObjectiveThis study proposes an advanced wearable VAG system integrating PID-controlled negative-pressure suction and real-time posture estimation to improve signal stability and enhance the assessment of knee joint mechanics during movement.MethodsThe proposed system combines a wearable acoustic sensing module with a vacuum-assisted suction cup and a PID controller that consistently maintains sensor adhesion. Real-time posture estimation is achieved using MediaPipe Pose, which computes knee flexion angles. VAG signals and kinematic data are synchronously acquired during OKC and CKC exercises for integrated analysis.ResultsIn this single-subject pilot test, the system achieved stable sensor attachment and repeatable vibroacoustic recordings throughout dynamic movements. Distinguishable frequency-domain characteristics were observed at relevant knee flexion angles, including approximately 30°, 63°, and 129°.ConclusionsThis pilot feasibility study demonstrates that the proposed wearable VAG platform can achieve stable acquisition of synchronized vibroacoustic and kinematic signals during dynamic knee movement in a single subject. These preliminary findings suggest potential future applications in osteoarthritis screening, rehabilitation assessment, and ambulatory monitoring, pending validation in larger, statistically powered cohorts.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261496167"},"PeriodicalIF":1.3,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AMSSE-SwinTrans: A harmonized multimodal hybrid framework with explainable AI for ischemic stroke lesion segmentation.","authors":"Prabha Susy Mathew, Anitha S Pillai, Lazzaro Di Biase","doi":"10.1177/09287329261495037","DOIUrl":"https://doi.org/10.1177/09287329261495037","url":null,"abstract":"<p><p>BackgroundAccurate ischemic stroke lesion segmentation from magnetic resonance imaging (MRI) is important but challenging due to variability in lesion morphology. Traditional convolutional neural networks (CNNs) effectively capture local spatial features but often struggle to capture long-range contextual dependencies needed for irregular lesions. Transformer-based architectures are good at capturing global context; however, they are computationally expensive and difficult to deploy in resource-constrained environments. The black-box nature of deep learning models limits their clinical adoption owing to lack of transparency.ObjectiveThis study introduces AMSSE-SwinTrans, a lightweight hybrid CNN-Transformer framework to make stroke lesion segmentation accurate, efficient and interpretable across different MRI modalities.MethodsThis study proposes AMSSE-SwinTrans, a lightweight hybrid CNN-Transformer framework that combines adaptive multi-scale squeeze-and-excitation (AMSSE) module with simplified transformer blocks employing multi-head self-attention, along with explainable AI (XAI) for clinical interpretation. The model was evaluated on a single-channel harmonized multimodal two-dimensional MRI Slices from ATLAS v2.0 and ISLES 2022 dataset.ResultsThe model achieved an overall Dice of 0.8629, lesion-specific Dice of 0.7535. An ablation study demonstrated a significant performance improvement when each component was added. The model comparisons were all significant with Wilcoxon signed-rank test yielding p-value (<i>p</i> < 0.001) across all comparisons. With only 27.67 M parameters, 17.67 GFLOPs, and 9.22 ms inference time per slice, the architecture is well-suited for deployment in resource-constrained clinical settings. The interpretability of the model is supported by Attention Rollout and Grad-CAM++ visualizations.ConclusionAMSSE-SwinTrans establishes itself as a resource-efficient and interpretable framework for multimodal stroke lesion segmentation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261495037"},"PeriodicalIF":1.3,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LTPat-based SOXFE: A self-organized explainable feature engineering model for automated fibromyalgia detection.","authors":"Yasemin Kirik, Serkan Kirik, Omer Faruk Goktas, Burak Tasci, Sengul Dogan, Turker Tuncer","doi":"10.1177/09287329261495183","DOIUrl":"https://doi.org/10.1177/09287329261495183","url":null,"abstract":"<p><p>BackgroundFibromyalgia is a chronic pain disorder associated with sleep disturbance and substantial functional burden.ObjectiveThis study evaluated a self-organized explainable feature engineering (SOXFE) model for EEG-based fibromyalgia classification.MethodsA previously reported sleep EEG dataset from a 32-participant source cohort was analyzed separately for NREM Stage 2 and Stage 3. The analysis-ready version contained 136 labeled stage-record files: 74 for Stage 2 and 62 for Stage 3. The SOXFE model comprised LTPat feature extraction, training-only CWNCA feature selection, tkNN classification, IMV information fusion, and DLob-based explanation. Record-wise LORO was primary; segment-level 10-fold cross-validation was complementary.ResultsRecord-wise LORO used 74 outer folds for Stage 2 and 62 for Stage 3. Descriptive pooled held-out-epoch accuracy was 92.77% (95% CI: 91.76-93.67) for Stage 2 and 97.00% (95% CI: 96.22-97.63) for Stage 3. Every epoch from the held-out recording was excluded from training and selection. Complementary segment-level 10-fold cross-validation yielded 100.00% accuracy for both stages but was not used as evidence of record-wise generalization.ConclusionsThe model provided record-disjoint classification and traceable model-derived explanations in this single-source dataset. LORO prevented within-record epoch leakage. However, participant linkage was unavailable, so records from the same person may have occurred across outer partitions and the reported values may overestimate person-independent performance. Verified subject-wise validation is required before clinical use.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261495183"},"PeriodicalIF":1.3,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal adaptive mirror therapy through gait adaptation on a split-belt treadmill for gait training.","authors":"Siyu Yang, Kangjie Zheng, Cenwei Li, Nanxin Li, Fred Han","doi":"10.1177/09287329261496166","DOIUrl":"https://doi.org/10.1177/09287329261496166","url":null,"abstract":"<p><p>BackgroundTraditional mirror therapy relies on fixed kinematic symmetry, often neglecting the trade-offs between movement accuracy, stability, and muscular effort. This study developed a Multimodal Adaptive Mirror Therapy system featuring a multi-objective controller for personalized gait rehabilitation.ObjectiveTo develop and evaluate a multimodal adaptive mirror therapy system that integrates gait kinematics, postural stability, and muscular activation into a unified multi-objective optimization framework for personalized gait rehabilitation.MethodsTwenty-six subjects underwent asymmetric gait training with unilateral limb restraint to simulate asymmetric gait patterns. The proposed system integrated camera-based gait data, inertial measurement unit (IMU)-based stability metrics, and electromyography (EMG) activation into a unified objective function. Optimization was dynamically adjusted via a weighted harmonic mean of multimodal scores. Normalized mutual information (NMI) was used to quantify dependencies, while interquartile range (IQR) analysis evaluated controller stability.ResultsNMI analysis revealed significant dependencies between all modalities (p < 0.05), with the training parameter showing the strongest correlation with stability (NMI = 0.342). The multimodal controller achieved balanced, near-optimal performance, yielding median scores of 97.81% for stability and 92.44% for gait. Notably, it exhibited the lowest inter-participant variability among all strategies in the stability domain (IQR: 24.80%), compared to substantially wider dispersions observed under unimodal controllers.ConclusionThis study demonstrated that a multimodal, closed-loop control scheme can effectively resolve performance trade-offs in gait training. By transitioning from rigid imitation to dynamic multi-objective optimization, the system provided a robust, intelligent closed-loop platform, establishing a practical foundation for patient-centered robotic rehabilitation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261496166"},"PeriodicalIF":1.3,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UCTE-OGC: Unsupervised contrastive text embedding with ontology-guided graph convolutions for biomedical clustering.","authors":"Meijing Li, Jiashun Guo, Runqing Huang","doi":"10.1177/09287329261495502","DOIUrl":"https://doi.org/10.1177/09287329261495502","url":null,"abstract":"<p><p>BackgroundEfficient organization and retrieval of rapidly growing biomedical literature require representations that capture contextual semantics and explicit biomedical relationships. However, existing methods typically model text and ontology structure separately, producing incomplete representations with limited cluster separability.ObjectiveThis study addresses the incomplete feature representation and low cluster separability that emerge as textual and ontological information are modeled independently, with the goal of enhancing clustering performance on biomedical literature.MethodsUCTE-OGC applies PubMedBERT with unsupervised contrastive learning to encode article titles and abstracts. In parallel, it learns relation-aware GO representations through DIFF-CR edge weighting, Node2Vec, and graph convolutional networks. An attention-based fusion module projects both representations into a shared latent space and optimizes their integration using a symmetric NT-Xent objective. The resulting embeddings are evaluated with K-Means, Gaussian mixture models, and spectral clustering.ResultsExperiments on a PubMed-derived dataset showed that UCTE-OGC produced higher observed clustering performance than the comparison methods under the reported settings and achieved its best results with spectral clustering. Ablation experiments indicated that contrastive learning and GO structural information made complementary contributions.ConclusionIntegrating contrastive text representations with relation-aware GO graph modeling provides effective embeddings for organizing and retrieving large biomedical literature collections.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261495502"},"PeriodicalIF":1.3,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven biosensor data fusion for multimodal activity recognition in health monitoring systems.","authors":"Iqra Aijaz Abro, Fatimah Alhayan, Hadeel Alsolai, Bayan Alabdullah, Ahmad Jalal, Hui Liu","doi":"10.1177/09287329261495179","DOIUrl":"https://doi.org/10.1177/09287329261495179","url":null,"abstract":"<p><p>BackgroundAI-driven healthcare monitoring systems are increasingly being developed to support elderly individuals through continuous and discreet monitoring of routine behavioral patterns.ObjectiveThis study aims to develop a multimodal framework for identifying mobility patterns by integrating inertial and vision-based sensor data to improve the accuracy and robustness of behavioral recognition.MethodsInertial data are denoised using a median filter, while video feeds are converted into grayscale frame sequences and enhanced using a Gaussian filter. Rectangular windowing is applied to inertial data, whereas U-Net is used for visual segmentation. Features are extracted using Gammatone Filter Cepstrum (GFC), Gaussian Likelihood Threshold Model (GLTM), Power Spectral Density (PSD), and temporal moments from inertial data, and Distance Transform, Gist, ORB, and SURF from visual data. The modalities are fused using shared class labels, followed by fuzzy-logic-based feature optimization and classification using a CNN-GRU hybrid model.ResultsThe proposed multimodal framework achieves 95.20% accuracy on the Continuous Multimodal Human Action Dataset (C-MHAD) and 94.79% on the multi-modal 3D human pose estimation dataset using mmWave, RGB-D, and inertial sensors (mRI) dataset, demonstrating improved behavioral categorization compared with unimodal approaches.ConclusionThe results highlight the potential of multimodal inertial and vision-based sensing for robust AI-assisted healthcare monitoring. However, evaluation is currently limited to public benchmark datasets and requires further validation in clinical settings.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261495179"},"PeriodicalIF":1.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting delirium in gastric surgery: An interpretable predictive model and statistical analysis in critical care.","authors":"Junqi Cui, Enoch Chi Ngai Lim, Chi Eung Danforn Lim","doi":"10.1177/09287329261495038","DOIUrl":"https://doi.org/10.1177/09287329261495038","url":null,"abstract":"<p><p>BackgroundPostoperative delirium is a serious complication in elderly ICU patients after gastric surgery, increasing mortality and healthcare burden. Early identification of high-risk patients may support timely prevention and optimise perioperative care.ObjectiveTo develop and externally validate an interpretable machine learning model for predicting postoperative delirium in elderly ICU patients undergoing gastric surgery.MethodsThis retrospective multicohort study used three perioperative and critical care databases. MIMIC-IV supported model development and internal validation, while eICU and MOVER supported external validation. Feature selection used LASSO regression, followed by comparison of six machine learning algorithms. Logistic regression was selected using the MIMIC-IV internal validation cohort based on discrimination, recall, and clinical interpretability; the locked model was then evaluated in eICU and MOVER. SHAP analysis quantified predictor contributions and enhanced model transparency.ResultsThe final population included four cohorts: MIMIC-IV training (<i>N</i> = 4316), MIMIC-IV internal validation (<i>N</i> = 1851), eICU external testing (<i>N</i> = 1745), and MOVER external testing (<i>N</i> = 1152). The internally selected logistic regression model achieved AUCs of 0.819 in eICU and 0.827 in MOVER, with recalls of 0.827 and 0.936, respectively. SHAP analysis identified chloride, sodium, bicarbonate, and anion gap as dominant predictors across cohorts.ConclusionThis study presents an interpretable postoperative delirium risk-prediction model in elderly ICU patients following gastric surgery. Although the model achieved high recall, marked class imbalance and low outcome prevalence were associated with low precision and a substantial false-positive burden. Prospective validation, threshold optimisation, and assessment of clinical utility are required before implementation.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261495038"},"PeriodicalIF":1.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable machine learning framework for congestive heart failure detection using GLCM-based HRV features and Bayesian network analysis.","authors":"Safa Habibullah, Seong-O Shim","doi":"10.1177/09287329261493210","DOIUrl":"https://doi.org/10.1177/09287329261493210","url":null,"abstract":"<p><p>BackgroundCongestive heart failure (CHF) is one of the main causes of cardiovascular morbidity and mortality. While conventional machine learning (ML) models demonstrate acceptable classification results, their operation is typically based on a black-box approach which does not allow to obtain an interpretable insight into the physiological mechanism of heart rate variability (HRV).ObjectiveThis paper suggests an approach which combines Gray-Level Co-occurrence Matrix (GLCM)-based texture analysis, Bayesian-optimized ML and Bayesian network inference to increase the accuracy and clinical interpretability of CHF prediction and provide a deeper understanding of physiological mechanisms.MethodsTwo-dimensional representation of HRV data taken from the PhysioNet database was used for extracting texture-based biomarkers using GLCM. Most discriminatory biomarkers were selected by the Kruskal-Wallis test and ML models were optimized by Bayesian approach. Performance of models was estimated by traditional classification metrics and Bayesian network analysis with posterior probabilities and sensitivity analysis of nonlinear dependencies between HRV texture-based features was performed.ResultsBest performance was achieved by Bayesian-optimized Quadratic Gaussian SVM, with 93.10% accuracy, 90.91% sensitivity, 94.44% specificity and AUC equal to 0.9697. The Bayesian network analysis revealed Dissimilarity, Cluster Prominence and Cluster Shade as the most important HRV texture biomarkers related to CHF.ConclusionThe novel XAI architecture is based on Bayesian optimization of ML along with Bayesian probability theory, which helps deliver precise and clinically relevant CHF diagnosis for early disease detection and personalized risk stratification.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":"9287329261493210"},"PeriodicalIF":1.3,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}