Medical Data Field Research Articles

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction. The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC. We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs. A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN. Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification

It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models.

MERGE: A Multi-graph Attentive Representation learning framework integrating Group information from similar patients

It is an important research task in the field of medical big data to predict patient’s future health status according to the historical temporal Electronic Health Records (EHRs). Most of the existing deep learning-based medical prediction methods only focus on the patient’s individual information. However, due to the sparseness and low quality of EHR data, individual clinical records of single patient often cannot provide complete health information, which severely limits the accuracy of the prediction models. In this paper, we propose a Multi-graph attEntive Representation learning framework integrating Group information from similar patiEnts(MERGE) for medical prediction. In this framework, while capturing the individual patient’s temporal characteristics through the individual representation learning module, the group representation leaning module is used to learn group representations of similar patients from different aspects as a supplement, thereby effectively improving the accuracy of patients’ representation. We evaluate our method on the MIMIC-III dataset for the task of in-hospital mortality prediction and Xiangya dataset for cardiovascular diseases (CVDs) prediction. The experimental results show that MERGE outperforms the state-of-the-art methods.

A Review of Blockchain-Based Secure Sharing of Healthcare Data

Medical data contains multiple records of patient data that are important for subsequent treatment and future research. However, it needs to be stored and shared securely to protect the privacy of the data. Blockchain is widely used in the management of healthcare data because of its decentralized and tamper-proof features. In order to study the development of blockchain in healthcare, this paper evaluates it from various perspectives. We analyze blockchain-based approaches from different application scenarios. These are blockchain-based electronic medical record sharing, blockchain and the Internet of Medical Things and blockchain-based federal learning. The results show that blockchain and smart contracts have a natural advantage in the field of medical data since they are tamper-proof and traceable. Finally, the challenges and future directions of blockchain in healthcare are discussed, which can help drive the field forward.

A Fine-Grained Medical Data Sharing Scheme with Ciphertext Reencryption

With the fantastic development of cloud computing technology, cloud storage has been widely applied in the field of medical data sharing due to its efficiency and flexibility. Attribute-based encryption (ABE), as a promising technology, plays an important role in the fine-grained sharing of medical data. However, there are two issues: (1) an access policy specified in the encryption phase may need to be updated after a period of time, and (2) the considerable decryption overhead in ABE is too heavy for resource-limited devices. To address these issues, a fine-grained medical data sharing scheme with ciphertext reencryption is proposed. Users with decryption permission can produce reencryption keys and cloud server can reencrypt initial ciphertext. The cloud server can predecrypt ciphertext, which reduces user’s decryption overhead. Moreover, the traditional centralized server is replaced with a decentralized blockchain that performs system initialization, key management, and user revocation. We compared the proposed scheme with some existing schemes in terms of function and efficiency; the results show that the efficiency of the proposed scheme outperforms other related schemes. The security analysis shows that the proposed scheme is security and correctness.

Low-latency cloud-fog network architecture and its load balancing strategy for medical big data

In order to apply fog computing to the field of medical big data, this paper proposes a low-latency hybrid cloud-fog network architecture for medical big data, which can solve the processing delay of business in cloud computing center architecture. In this architecture, edge network equipment such as routers and switches in the hospital are used to build a “fog computing” layer between the cloud server and terminals. Then, the computing service for medical data on the cloud is moved to fog equipment for processing, which reduces the processing delay of medical businesses. Besides, it reduces the computing load on cloud servers and improves the overall robustness of network. For further optimizing the processing delay of business in the above network architecture, we study the load balancing strategy in fog computing network. Due to the global search ability of bat algorithm is strong, the convergence speed is fast and it is easy to implement. Therefore, this paper uses bat algorithm to solve the optimization problem in medical big data scenario. Bat algorithm is better than genetic algorithm and particle swarm optimization on the unconstrained optimization problems. However, it also suffers from problems such as local optimization and slow convergence. To solve this problem, we utilize load balancing to initialize bat population data that improves the quality of solution for initial samples. After getting the best bats, a Powell local search is performed on them, which speeds up the convergence of our algorithm. Finally, simulation results show that the proposed hybrid cloud-fog network architecture can reduce the processing delay of medical big data and improve user experience effectively.

The “No Sampling Parameter Estimation (NSPE)” algorithm for stochastic differential equations

The parameter estimation problem in stochastic differential equations (SDEs) has gained much attention in recent years due to their increased applications in the field of pharmaceuticals, ecosystem modeling, and medical data like EKG, blood pressure, and sugar levels. The predictive power of SDEs lie in the choice of parameter values that describe the real data effectively. The classical SDE parameter estimation methods are largely based on likelihood estimation, which is computationally expensive. In this work, we propose a relatively simplified approach based on deterministic nonlinear optimization method which does not require sampling. The results from the suggested No Sampling Parameter Estimation (NSPE) algorithm for selected examples are compared with the data, results from deterministic ODE (ordinary differential equation) model and traditional methods of maximum likelihood estimation (MLE) and generalized method of moments (GMM) for SDEs. The NSPE algorithm is more accurate and reduces computation time significantly when compared to the traditional methods.

A Hybrid Binary Cuckoo Search and Genetic Algorithm for Feature Selection in Type-2 Diabetes

Data mining techniques are applied in bioinformatics to analyze biomedical data. When the number of features related to the data is irrelevant, the classifiers will produce unsatisfactory results. This paper addresses the need to analyze the data for extracting relevant features. A number of feature selection algorithms are developed in the field of medical data to address feature selection. In this paper, an intelligent hybrid optimal feature selection algorithm is proposed for Type-2 diabetes with improved classification accuracy. This work proposes an intelligent Hybrid Binary Cuckoo Search (CS) and Genetic Algorithm (GA) for selecting the important features of Type-2 diabetes. In HBCS-GA, exploration and exploitation of CS is improved using genetic operators to select relevant features with better accuracy. To validate the model, a 10-fold cross-validation strategy is used. The proposed algorithm produces 99.31% accuracy to diagnose the disease. The performance of HBCS-GAis also compared with other approaches. Also, the model validation with reduced features is performed with Decision Tree(DT), Bayesian Network(BN), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. The accuracies obtained are 94.46%, 96.07%, 98.84%, 96.79% respectively. The results also showed that HBCS-GA achieved high classification accuracy than the other approaches. Keywords: Diabetes, normalization, classification, instance selection, wrapper feature selection, cuckoo search, genetic algorithm.

A New Clustering Segmentation Algorithm of 3D Medical Data Field Based on Data Mining

Direct 3D volume segmentation is one of the difficult and hot research fields in 3D medical data field processing. Using the clustering and analyzing techniques of data mining, a new clustering and segmentation algorithm for 3D medical image based on density-isoline is presented. Firstly, According to the physical means of the medical data, the voxel’s gray level value in data field is redefined to speed up succeed processing. Secondly, the paper analyzes and improves the clustering and segmentation algorithm through preclustering image, defineing seeds and selecting process cells to improve algorithm efficiency. The experimental results show that the time consuming of the algorithm is only one tenth of the traditional methods and the algorithm has high accuracy when used to segment complicated 3D medical tissue.

A New 3D Medical Data Field Segmentation Algorithm Based on Improved K_Means Clustering

Direct 3D volume segmentation is one of the difficult and hot research fields in 3D medical data field processing. Using K-means clustering techniques, a new clustering segmentation algorithm is presented. Firstly, According to the physical means of the medical data, the data field is preprocessed to speed up succeed processing. Secondly, the paper deduces and analyzes the clustering and segmentation algorithm and presents some methods to increase the process speed, including improving cluster seed selection, improving calculation flow, and amending pixel processing and operational principle of algorithm. Finally, the experimental results show that the algorithm has high accuracy when used to segment 3D medical tissue and can improve process speed greatly.