IEEE Journal of Biomedical and Health Informatics最新文献

The research on identifying drug-disease associations (DDAs) is widely used in scenarios such as drug development, clinical decision-making, and drug repurposing, holding significant biological and medical significance. Existing methods for drug-disease association prediction have achieved decent performance, they primarily rely on simplistic drug-disease association graphs or similarity graphs. These methods often struggle to capture the high-order correlations of complex multimodal data, limiting their ability to handle the complexity of data associations effectively. In addition, real drug-disease associations are highly sparse, posing a significant challenge to prediction accuracy. To tackle these issues, we propose a general hypergraph neural network framework for drug-disease association prediction based on hierarchical contrastive learning and cross-attention learning. It leverages hypergraph neural networks to learn representations of drugs and diseases carrying high-order correlations and strengthens representation quality using interactive attention learning and hierarchical contrastive learning. Meanwhile, the -weighted loss function is utilized to adapt to the high sparsity property of real drug-disease associations during model training and improve prediction performance. Extensive experiments demonstrate that DD-HGNN surpasses other state-of-the-art methods in predicting drug-disease associations and further validation through case studies on Leukemia and Colorectal Neoplasms underscores its reliability.

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software.

Accurate and timely core body temperature measurement is essential for identifying and preventing heat-related illnesses. Infrared thermography (IRT) provides a non-invasive, full-scale and efficient temperature path for body temperature screening. However, the complexity of environmental factors and personal features continuously affect the measured skin temperature, resulting in low accuracy and reliability of existing body temperature monitoring by IRT. To address this issue, this study proposed an innovative core temperature prediction model (CTPM) for IRT based on heat transfer mechanism between the human body and the ambient environment. Based on human body thermoregulation, the optimal facial thermal feature that can reflect the impact of ambient temperature on skin temperature is proposed. Combining it with personal features and distributed facial skin temperature features, a CTPM is established based on Random Forest algorithm. The proposed CTPM are evaluated using a publicly available PhysioNet facial and oral temperature dataset. The results demonstrate that the proposed optimal CTPM achieves the best accuracy and consistency in predicting core body temperature. The root-mean-square error of the optimal CTPM is 0.259°C, and the mean lower and upper 95% limits of agreement are -0.505 °C and 0.507°C, respectively. Variable importance analysis indicates that the proposed optimal facial thermal feature makes a dominant contribution to the prediction performance of the optimal CTPM. Our method enables accurate and stable core body temperature prediction in complex ambient environments over a wide range of temperatures, and has the potential to replace traditional contact measurements to meet clinical needs.

Acoustic foundation models, through self-supervised learning on large amounts of unlabeled speech data, can acquire rich acoustic representations. In recent years, these models have demonstrated substantial potential in audio-based health-related tasks, remarkably enhancing the efficiency and quality of healthcare services and contributing to the advancement of smart healthcare. However, there is currently a lack of systematic research and exploration on the performance of acoustic foundation models in health-related tasks. Furthermore, inconsistencies in evaluation methods and experimental setups hinder fair comparisons between different methods, severely impeding progress in this field. To address these challenges, we establish a unified Benchmark for evaluating Bio-Acoustic health via acoustic foundation models, namely BAHBench. BAHBench encompasses 6 distinct health-related tasks and evaluates 12 acoustic foundation models within a unified evaluation framework and parameter settings, enabling fair comparisons across different models. Our objective is to explore the effectiveness of current acoustic foundation models in health-related tasks. Thus, we discuss the impact of model size and data diversity on performance, and investigate feature selection and efficient fine-tuning strategy. Experimental results show that different health-related tasks benefit from features from different layers of the foundation model, while LoRA fine-tuning further enhances the model's performance on downstream tasks. Our goal is to provide clear and comprehensive guidance for future researchers. The code related to this study will be available to the research community to promote transparency and reproducibility.

Prostate cancer is a critical healthcare challenge globally and is one of the most prevalent types of cancer in men. Early and accurate diagnosis is essential for effective treatment and improved patient outcomes. In the existing literature, computer-aided diagnosis (CAD) solutions have been developed to assist pathologists in various tasks, including classification, diagnosis, and prostate cancer grading. Content-based image retrieval (CBIR) techniques provide valuable approaches to enhance these computer-aided solutions. This study evaluates how generative deep learning models can improve the quality of retrievals within a CBIR system. Specifically, we propose applying a Siamese Network approach, which enables us to learn how to encode image patches into latent representations for retrieval purposes. We used the ProGleason-GAN framework trained on the SiCAPv2 dataset to create similar pairs of input patches. Our observations indicate that introducing synthetic patches leads to notable improvements in the evaluated metrics, underscoring the utility of generative models within CBIR tasks. Furthermore, this work is the first in the literature where latent representations optimized for CBIR are used to train an attention mechanism for performing Gleason Scoring of a WSI.

In prostate 3D MRI image segmentation methods, it is usually necessary to annotate each slice, and these annotations are generally time-consuming and specialized. In this study, we generate pseudo-labels using an annotation method with one foreground seed point and six edge relaxation points. We design a weakly supervised semantic learning segmentation framework, ACEA-Net. This segmentation framework solves the under-expansion problem due to the lack of semantic affinity of the seed point pixels in the pseudo-labeling generation process. We design a Seed Cluster Geodesic Distance Transform (SeedGeo) seed expansion strategy to provide a more complete supervised signal. In the segmentation model training phase, Adaptive Convolutional Normalization (ACN) and Enhanced Simple Parameter-Free Attention Module (SimAM) are utilized to smooth the convolutional layer's output in the U-Net baseline model to suppress noisy labels. The proposed segmentation framework achieves excellent segmentation results on the MSD prostate and PROMISE12 prostate datasets, with Dice similarity coefficients (Dice) of 87.23% and 81.00% for the two segmentation tasks, and Average Symmetry Surface Distances (ASSD) of 1.73mm and 2.02mm, respectively, which are superior to the current state-of-the-art method.

Medical video segmentation is fundamentally important in clinical diagnosis and treatment procedures, offering dynamic tracking of breast lesions across frames in ultrasound videos for improved segmentation performance. However, existing approaches face challenges in striking a balance between segmentation performance and inference speed, hindering real-time application in resource-constrained medical environments. In order to address these limitations, we present BaS, a blazing-fast on-device breast lesion segmentation model. BaS integrates the Stem module and BaSBlock to refine representations through inter- and intra-frame analysis on ultrasound videos. In addition, we release two versions of BaS: the BaS-S for superior segmentation performance and the BaS-L for accelerated inference times. Experimental Results indicate that BaS surpasses the top-performing models in terms of segmenting efficiency and accuracy of predictions on devices with limited resources. This work advances the development of efficient medical video segmentation frameworks applicable to multiple medical platforms.

Photoplethysmography (PPG) is commonly used to gather health-related information but is highly affected by motion artifacts from daily activities. Inspired by the strong denoising capabilities and generalization of diffusion probabilistic models, this paper proposes a novel PPG denoising method using a diffusion probabilistic model to reduce the impact of these artifacts. While typical diffusion models handle Gaussian noises, motion artifacts often involve non-Gaussian noise. To address this, the proposed method incorporates noisy PPG signals into both the diffusion and reverse processes, allowing the model to adapt better to complex and non-Gaussian noises. A dataset with clean and noisy PPG signals from 15 subjects performing various motion tasks was collected for evaluation. The results show the proposed model significantly improves PPG signal quality, reducing the Peak-Rejection-Rate (PRR) from 0.24 to 0.03. It also enhances the accuracy of heart rate (HR) estimation and various heart rate variability (HRV) measures, showing robustness and good generalization across different tasks and subjects.

Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into "realistic" healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92.50 2.70%.

Computer-aided detection through deep learning is becoming a prevalent approach across various fields, including detection of anomalies in medical procedures. One such medical procedure involves the placement of medical tubes to provide nutrition or other medical procedures in critically ill patients. Medical tube placement can be highly complex and prone to subjective errors. Malposition of medical tubes is often observed, and associated with significant morbidity and mortality. In addition, continuous verification using manual procedures such as capnography, pH testing, auscultation, and visual inspection through chest X-ray (CXR) imaging is required. In this paper, we propose a Conv-MTD, a medical tube detection (MTD) model that detects the placement of medical tubes using CXR images, assisting radiologists with precise identification and categorizing the tubes into normal, abnormal, and borderline placement. Conv-MTD leverages the state-of-the-art EfficientNet-B7 architecture as its backbone, enhanced with auxiliary head in the intermediate layers to mitigate vanishing gradient issues common in deep neural networks. The Conv-MTD is further optimized using post-training 16-bit floating-point (FP16) quantization, which effectively reduces memory consumption and inference latency on resource-constrained devices. Conv-MTD provided the best performance, with an average area under the receiver-operator curve AUC-ROC of 0.95. The proposed Conv-MTD has the potential to operate on resource-constrained point-of-care devices, enabling low-cost and automated assessments in various healthcare settings.