Medical & Biological Engineering & Computing最新文献_第6页

Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG. 利用深度学习方法和脑电图解码蹬车任务中的下肢运动参数。

IF 4.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-07-19 DOI: 10.1007/s11517-024-03147-3

Cristian Felipe Blanco-Diaz, Cristian David Guerrero-Mendez, Rafhael Milanezi de Andrade, Claudine Badue, Alberto Ferreira De Souza, Denis Delisle-Rodriguez, Teodiano Bastos-Filho

{"title":"Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.","authors":"Cristian Felipe Blanco-Diaz, Cristian David Guerrero-Mendez, Rafhael Milanezi de Andrade, Claudine Badue, Alberto Ferreira De Souza, Denis Delisle-Rodriguez, Teodiano Bastos-Filho","doi":"10.1007/s11517-024-03147-3","DOIUrl":"10.1007/s11517-024-03147-3","url":null,"abstract":"Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3763-3779"},"PeriodicalIF":4.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141724856","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}

引用次数: 0

Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis. 利用步态分析特征选择增强老年人跌倒风险预测能力

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-08-10 DOI: 10.1007/s11517-024-03180-2

Sabri Altunkaya

{"title":"Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.","authors":"Sabri Altunkaya","doi":"10.1007/s11517-024-03180-2","DOIUrl":"10.1007/s11517-024-03180-2","url":null,"abstract":"There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors or activities to achieve higher accuracy. However, multiple sensors and activities reduce the availability of these systems. This study aims to develop a system to perform fall prediction for the elderly by using signals recorded from a single sensor during a short-term activity. A total of 168 features in the time and frequency domains were created using acceleration signals obtained from 71 elderly people. The features were weighted based on the ReliefF algorithm, and the artificial neural networks model was developed using the most important features. The best classification result was obtained using the 17 most important features of those weighted for K = 20 nearest neighbors. The highest accuracy was 82.2% (82.9% Sensitivity, 81.6% Specificity). The partially high accuracy obtained in our study shows that falling can be detected early with a sensor and a simple activity by determining the right features and can be easily applied in the assessment of the elderly during routine follow-ups.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3887-3897"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141914377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-file dynamic compression method based on classification algorithm in DNA storage. 基于 DNA 存储分类算法的多文件动态压缩方法。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-12-01 Epub Date: 2024-06-26 DOI: 10.1007/s11517-024-03156-2

Kun Bi, Qi Xu, Xin Lai, Xiangwei Zhao, Zuhong Lu

{"title":"Multi-file dynamic compression method based on classification algorithm in DNA storage.","authors":"Kun Bi, Qi Xu, Xin Lai, Xiangwei Zhao, Zuhong Lu","doi":"10.1007/s11517-024-03156-2","DOIUrl":"10.1007/s11517-024-03156-2","url":null,"abstract":"The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"3623-3635"},"PeriodicalIF":2.6,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452048","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}

引用次数: 0

Artificial blood-hope and the challenges to combat tumor hypoxia for anti-cancer therapy.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-30 DOI: 10.1007/s11517-024-03233-6

Rishabh Sharma, Manju Kashyap, Hatem Zayed, Lucky Krishnia, Manoj Kumar Kashyap

{"title":"Artificial blood-hope and the challenges to combat tumor hypoxia for anti-cancer therapy.","authors":"Rishabh Sharma, Manju Kashyap, Hatem Zayed, Lucky Krishnia, Manoj Kumar Kashyap","doi":"10.1007/s11517-024-03233-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03233-6","url":null,"abstract":"The blood plays a vital role in the human body and serves as an intermediary between various physiological systems and organs. White blood cells, which are a part of the immune system, defend against infections and regulate the body temperature and pH balance. Blood platelets play a crucial role in clotting, the prevention of excessive bleeding, and the promotion of healing. Blood also serves as a courier system that transports hormones to facilitate communication and synchronization between different organs and systems in the body. The circulatory system, comprised of arteries, veins, and capillaries, plays a crucial role in the efficient transportation and connection of vital nutrients and oxygen. Despite the importance of natural blood, there are often supply shortages, compatibility issues, and medical conditions, which make alternatives such as artificial blood necessary. This is particularly relevant in cancer treatment, which was the focus of our study. In this study, we investigated the potential of artificial blood in cancer therapy, specifically to address tumor hypoxia. We also examined the potential of red blood cell substitutes such as hemoglobin-based oxygen carriers and perfluorocarbons. Additionally, we examined the production of hemoglobin using E. coli and the role of hemoglobin in oncogenesis. Furthermore, we explored the potential use of artificial platelets for cancer treatment. Our study emphasizes the significance of artificial blood in improving cancer treatment outcomes.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755760","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}

引用次数: 0

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-29 DOI: 10.1007/s11517-024-03246-1

Yufei Yang, Mingai Li, Linlin Wang

{"title":"An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.","authors":"Yufei Yang, Mingai Li, Linlin Wang","doi":"10.1007/s11517-024-03246-1","DOIUrl":"https://doi.org/10.1007/s11517-024-03246-1","url":null,"abstract":"Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and self-updating capability. Broad learning system (BLS) can be remodeled in an efficient incremental learning way. However, its architecture is intractable to change automatically to adapt to new incoming MI-EEG with time-varying and complex temporal-spatial characteristics. In this paper, an adaptive session-incremental BLS (ASiBLS) is proposed based on mutual information theory and BLS. For the initial session data, a compact temporal-spatial feature extractor (CTS) is designed to acquire the temporal-spatial features, which are input to a baseline BLS (bBLS). Furthermore, for new session data, a mutual information maximization constraint (MIMC) is introduced into the loss function of CTS to make the features' probability distribution sufficiently similar to that of the previous session, a new incremental BLS sequence (iBLS) is obtained by adding a small number of nodes to the previous model, and so on. Experiments are conducted based on the BCI Competition IV-2a dataset with two sessions and IV-2b dataset with five sessions, ASiBLS achieves average decoding accuracies of 79.89% and 87.04%, respectively. The kappa coefficient and forgetting rate are also used to evaluate the model performance. The results show that ASiBLS can adaptively generate an optimized and reduced model for each session successively, which has better plasticity in learning new knowledge and stability in retaining old knowledge as well.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752060","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}

引用次数: 0

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-29 DOI: 10.1007/s11517-024-03252-3

Yanzhou Dai, Qiang Wang, Shulin Cui, Yang Yin, Weibo Song

{"title":"MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.","authors":"Yanzhou Dai, Qiang Wang, Shulin Cui, Yang Yin, Weibo Song","doi":"10.1007/s11517-024-03252-3","DOIUrl":"https://doi.org/10.1007/s11517-024-03252-3","url":null,"abstract":"The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low computational efficiency when addressing this task. To overcome these issues, this paper introduces an innovative lightweight 3D medical image segmentation network-MediLite3DNet. The core of this network is the Parallel Multi-Scale High-Resolution Network (PMHNet), which effectively retains detailed features of the airway and optimizes the fusion of multi-scale features through its parallel structure. In response to the complexity of existing networks and their reliance on vast amounts of training data, this paper presents an efficient Hierarchical Decoupled Convolution Module (EHDC) to reduce computational costs while maintaining efficient feature extraction capabilities. Furthermore, to enhance the accuracy of segmentation, a lightweight Channel and Spatial Attention Mechanism (LCSA) is proposed. This mechanism identifies and emphasizes key channels and spatial features, improving the processing of complex medical images while controlling the increase in the number of parameters. Experiments conducted on a clinical CT dataset demonstrate the network's exceptional performance, with a Dice coefficient of 97.42%, sensitivity of 98.69%, and Jaccard index of 95%. Maintaining high precision, the model has a parameter count of only 0.227M and a floating-point operation count (FLOPs) of 24.526G, proving its computational efficiency. The significance of this study is that it provides a highly efficient and accurate diagnostic tool for children with adenoid hypertrophy. Additionally, with the innovative MediLite3DNet design, it brings a new lightweight solution to the domain of medical image segmentation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752065","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}

引用次数: 0

CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-29 DOI: 10.1007/s11517-024-03247-0

Kai Zhang, Wei Liang, Peng Cao, Zhaoyang Mao, Jinzhu Yang, Osmar R Zaiane

{"title":"CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.","authors":"Kai Zhang, Wei Liang, Peng Cao, Zhaoyang Mao, Jinzhu Yang, Osmar R Zaiane","doi":"10.1007/s11517-024-03247-0","DOIUrl":"https://doi.org/10.1007/s11517-024-03247-0","url":null,"abstract":"Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis. However, most previous studies neither effectively learn label correlations nor take full advantage of them to improve multi-label classification performance. In addition, different labels of CXR images are usually severely imbalanced, resulting in the model exhibiting a bias towards the majority class. To address these challenges, we introduce a framework that not only learns label correlations but also utilizes them to guide the learning of features and the process of oversampling. In this paper, our approach incorporates self-attention to capture high-order label correlations and considers label correlations from both global and local perspectives. Then, we propose a consistency constraint and a multi-label contrastive loss to enhance feature learning. To alleviate the imbalance issue, we further propose an oversampling approach that exploits the learned label correlation to identify crucial seed samples for oversampling. Our approach repeats 5-fold cross-validation process experiments three times and achieves the best performance on both the CheXpert and ChestX-Ray14 datasets. Learning accurate label correlation is significant for multi-label classification and taking full advantage of label correlations is beneficial for discriminative feature learning and oversampling. A comparative analysis with the state-of-the-art approaches highlights the effectiveness of our proposed methods.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752064","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}

引用次数: 0

CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images. CC-TransXNet：用于从眼底图像自动分割视杯和视盘的混合 CNN 变换器网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-27 DOI: 10.1007/s11517-024-03244-3

Zhongzheng Yuan, Jinke Wang, Yukun Xu, Min Xu

{"title":"CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images.","authors":"Zhongzheng Yuan, Jinke Wang, Yukun Xu, Min Xu","doi":"10.1007/s11517-024-03244-3","DOIUrl":"https://doi.org/10.1007/s11517-024-03244-3","url":null,"abstract":"Accurate segmentation of the optic disk (OD) and optic cup (OC) regions of the optic nerve head is a critical step in glaucoma diagnosis. Existing architectures based on convolutional neural networks (CNNs) still suffer from insufficient global information and poor generalization ability to small sample datasets. Besides, advanced transformer-based models, although capable of capturing global image features, perform poorly in medical image segmentation due to numerous parameters and insufficient local spatial information. To address the above two problems, we propose an innovative W-shaped hybrid network framework, CC-TransXNet, which combines the advantages of CNN and transformer. Firstly, by employing TransXNet and improved ResNet as feature extraction modules, the network considers local and global features to enhance its generalization ability. Secondly, the convolutional block attention module (CBAM) is introduced in the residual structure to improve the ability to recognize the OD and OC by applying attention in both the channel and spatial dimensions. Thirdly, the Contextual Attention (CoT) self-attention mechanism is used in the skip connection to adaptively allocate attention to the contextual information, further enhancing the segmentation's accuracy. We conducted experiments on four publicly available datasets (REFUGE 2, RIM-ONE DL, GAMMA, and Drishti-GS). Compared with the traditional U-Net, CNN, and transformer-based networks, our proposed CC-TransXNet improves the segmentation accuracy and significantly enhances the generalization ability on small datasets. Moreover, CC-TransXNet effectively controls the number of parameters in the model through optimized design to avoid the risk of overfitting, proving its potential for efficient segmentation.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734360","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}

引用次数: 0

A new sensing paradigm for the vibroacoustic detection of pedicle screw loosening. 振动声学检测椎弓根螺钉松动的新传感范例。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-19 DOI: 10.1007/s11517-024-03235-4

Matthias Seibold, Bastian Sigrist, Tobias Götschi, Jonas Widmer, Sandro Hodel, Mazda Farshad, Nassir Navab, Philipp Fürnstahl, Christoph J Laux

{"title":"A new sensing paradigm for the vibroacoustic detection of pedicle screw loosening.","authors":"Matthias Seibold, Bastian Sigrist, Tobias Götschi, Jonas Widmer, Sandro Hodel, Mazda Farshad, Nassir Navab, Philipp Fürnstahl, Christoph J Laux","doi":"10.1007/s11517-024-03235-4","DOIUrl":"10.1007/s11517-024-03235-4","url":null,"abstract":"The current clinical gold standard to assess the condition and detect loosening of pedicle screw implants is radiation-emitting medical imaging. However, solely based on medical imaging, clinicians are not able to reliably identify loose implants in a substantial amount of cases. To complement medical imaging for pedicle screw loosening detection, we propose a new methodology and paradigm for the radiation-free, non-destructive, and easy-to-integrate loosening detection based on vibroacoustic sensing. For the detection of a loose implant, we excite the vertebra of interest with a sine sweep vibration at the spinous process and use a custom highly sensitive piezo vibration sensor attached directly at the screw head to capture the propagated vibration characteristics which are analyzed using a detection pipeline based on spectrogram features and a SE-ResNet-18. To validate the proposed approach, we propose a novel, biomechanically validated simulation technique for pedicle screw loosening, conduct experiments using four human cadaveric lumbar spine specimens, and evaluate our algorithm in a cross-validation experiment. The proposed method reaches a sensitivity of <math><mrow><mn>91.50</mn> <mo>±</mo> <mn>6.58</mn> <mo>%</mo></mrow> </math> and a specificity of <math><mrow><mn>91.10</mn> <mo>±</mo> <mn>2.27</mn> <mo>%</mo></mrow> </math> for pedicle screw loosening detection.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669765","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}

引用次数: 0

TCKAN: a novel integrated network model for predicting mortality risk in sepsis patients. TCKAN：预测败血症患者死亡风险的新型综合网络模型。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2024-11-19 DOI: 10.1007/s11517-024-03245-2

Fanglin Dong, Shibo Li, Weihua Li

{"title":"TCKAN: a novel integrated network model for predicting mortality risk in sepsis patients.","authors":"Fanglin Dong, Shibo Li, Weihua Li","doi":"10.1007/s11517-024-03245-2","DOIUrl":"10.1007/s11517-024-03245-2","url":null,"abstract":"Sepsis poses a major global health threat, accounting for millions of deaths annually and significant economic costs. Accurately predicting the risk of mortality in sepsis patients enables early identification, promotes the efficient allocation of medical resources, and facilitates timely interventions, thereby improving patient outcomes. Current methods typically utilize only one type of data-either constant, temporal, or ICD codes. This study introduces a novel approach, the Time-Constant Kolmogorov-Arnold Network (TCKAN), which uniquely integrates temporal data, constant data, and ICD codes within a single predictive model. Unlike existing methods that typically rely on one type of data, TCKAN leverages a multi-modal data integration strategy, resulting in superior predictive accuracy and robustness in identifying high-risk sepsis patients. Validated against the MIMIC-III and MIMIC-IV datasets, TCKAN surpasses existing machine learning and deep learning methods in accuracy, sensitivity, and specificity. Notably, TCKAN achieved AUCs of 87.76% and 88.07%, demonstrating superior capability in identifying high-risk patients. Additionally, TCKAN effectively combats the prevalent issue of data imbalance in clinical settings, improving the detection of patients at elevated risk of mortality and facilitating timely interventions. These results confirm the model's effectiveness and its potential to transform patient management and treatment optimization in clinical practice. Although the TCKAN model has already incorporated temporal, constant, and ICD code data, future research could include more diverse medical data types, such as imaging and laboratory test results, to achieve a more comprehensive data integration and further improve predictive accuracy.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669767","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}

引用次数: 0