RETRACTED ARTICLE: Secure prediction and assessment of sports injuries using deep learning based convolutional neural network

CEFEs: A CNN Explainable Framework for ECG Signals

A large number of patient healthcare data have been collected in the process of diagnosis and treatment of intensive care medicine, which provides major benefits for patient safety and quality. Unfortunately, the application of medical data is greatly limited. Key barriers to the use of the data include difficulties in data extraction and cleaning, and the construction of high-quality data sets promotes the research of medical big data analysis. In China, there is few intensive care data set built by clinicians has been used for clinical outcome prediction. This study developed and evaluated an Intensive Care Medical (ICM) data set for critically care patients that can be used for deep learning. The ICM data set contained four types of data collected routinely in Chinese hospitals, including all-cause characteristics of administrative information, vital signs, laboratory tests, and intravenous medication records. A total of 17,291 ICU admissions involving 12,815 patients aged 14 years and older were extracted from the data set. Deep learning model achieved high accuracy for tasks in hospital mortality predicting (AUROC[area under the receiver operator curve] reach 0.8941). We believe that the ICM data set can be used to create accurate predictions for a variety of clinical scenarios.

Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data. However, networks trained on synthetic data often fail to generalize to real data. Cinematic rendering simulates the propagation and interaction of light passing through tissue models reconstructed from CT data, enabling the generation of photorealistic images. In this paper, we present one of the first applications of cinematic rendering in deep learning, in which we propose to fine-tune synthetic data-driven networks using cinematically rendered CT data for the task of monocular depth estimation in endoscopy. Our experiments demonstrate that: (a) convolutional neural networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model. Our empirical evaluation demonstrates that networks fine-tuned with cinematically rendered data predict depth with 56.87% less error for rendered endoscopy images and 27.49% less error for real porcine colon endoscopy images.

Recent technologies such as Big Data could be used in order to help the improvement of other fields except for the telecommunication field. Due to the Health field, the Big Data technology could contribute with the aim to help for the purpose of analysis and management of the huge amounts of health data. The main objective of this research proposal is an analytical study of the technologies IoT, Cloud Computing (CC) and large-scale data (Big Data) to resolve various issues facing the health sector in relation to these technologies. The purpose of the research proposal is the collection of medical (e-health) big data in real time. The collection will be performed by sensor devices and actuators, which will wear patients who suffer from various ailments. Then is following, the transfer of these data through a network to a cloud server. Additionally, these data will be processed in the cloud which makes its analysis so as to become meaningless. By the analysis of these data is done the data mining. Finally, to address the various problems in the health sector, the transfer of the analyzed health data will be held by the devices of the relevant persons. Also, in our study, we will deal with the security of medical data which constitute personal data and must be protected.

At present, how to make use of massive medical information resources to provide scientific decision-making for the diagnosis and treatment of diseases, summarize the curative effect of various treatment schemes, and better serve the decision-making management, medical treatment, and scientific research, has drawn more and more attention of researchers. Deep learning, as the focus of most concern by both academia and industry, has been effectively applied in many fields and has outperformed most of the machine learning methods. Under this background, deep learning based medical data analysis emerged. In this survey, we focus on reviewing and then categorizing the current development. Firstly, we fully discuss the scope, characteristic and structure of the heterogeneous medical data. Afterward and primarily, the main deep learning models involved in medical data analysis, including their variants and various hybrid models, as well as main tasks in medical data analysis are all analyzed and reviewed in a series of typical cases respectively. Finally, we provide a brief introduction to certain useful online resources of deep learning development tools.

In the latest progress in healthcare, the continuous creation of digital medical information is an important basis for the analysis of large medical data. This paper proposes a medical big data analysis algorithm based on access control system that provides reliable security protection to big medical data (BMD) by considering various quantitative parameters. The proposed algorithm calculates the reputation values of different users, which correspond to each parameter. Based on the calculated reputation values, the proposed algorithm grants access authority to each user. Simulation results are performed to verify the effectiveness of security system solving protection of the sensitive personal information of patients.

Medical organizations struggle to deal with huge high-dimensional datasets that need powerful machine learning systems to produce precise healthcare outcomes. Traditional analytical techniques prove inadequate when dealing with extraction from features and performance of classifiers in this specific setting. The research introduces an algorithm which enhances Stacked Autoencoders (SAEs) by combining them with a customized Logistic Regression model intended for medical high-dimensional data analysis. This approach implements a Hybrid Imputation Method using MICE and KNN Imputation which precedes other stages and helps process missing values and outliers in medical data. We use CNNs and SAEs together for deep feature extraction before using Feature Fusion to assemble a robust feature collection. A set of the most important features is identified by executing Advanced Ensemble Feature Selection (EFS) procedures which include Few-shot Learning and Model-Agnostic Meta-Learning Algorithm (MAML) and Genetic Algorithm-Based Feature Selection (GAFS). The procedure of fine-tuning pre-trained models represents an effective enhancement for classification tasks particularly in situations of limited dataset availability. The experimental outcomes demonstrate remarkable performance gains in terms of accuracy and sensitivity and specificity as well as reduced execution time as compared to current techniques. Upcoming work for this study involves speeding up algorithm processing abilities and scalability alongside the integration of robust deep learning structures with self-supervised learning methodologies together with upgrade transfer learning approaches for medical dataset variety applications. The study will concentrate on enhancing model transparency through explainable AI and real-time validation for clinical deployment and ethical and regulatory compliance to develop this technique for practical healthcare settings.

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

Medical data is increasing rapidly. The rapid fusion of medical record text data, medical laboratory data and image data in many regional hospitals have brought light to the screening, diagnosis and treatment of diseases. Medical text data records the patient's detailed condition and treatment process, with abundant information. As a branch of artificial intelligence, deep learning is gradually penetrating the medical field. The application of deep learning in the medical field shows great application prospects. Therefore, mining the knowledge contained in medical texts can provide better medical services for the majority of patients, which has good theoretical and practical significance. In this paper, the content of medical texts was analyzed, and the test questions and standard answers of the USMLE Step 2 Clinical Skills Exam were used as data sets to explore the core content of the medical chief texts. After comparing and analyzing different methods, such as BERT and DeBERTa, an intelligent follow-up system based on DeBERTa was finally proposed, hoping to help clinicians read medical data more effectively and weaken the problem of poor diagnosis results caused by personal academic ability differences.

Transforming the non-structured medical text data into structured data is the basis of the processing and analysis of medical data. The effect of general-purpose word segmentation tools recognizing terminology is not ideal, which greatly affects the accuracy of the word segmentation, and further influences the result of text structuralization. In view of above problems, this paper puts forward a method of discovering new words based on word embedding. It uses Google open source word vector tool word2vec to train text and map the words into abstracted n-dimensional vector space. We can get the latent semantic relations between words and words in the corpus. And then combining the information entropy and word frequency, we can find new words. Finally, we design information extraction rules to get the key information according to the new words, and organize them into structured data. Experimental results on real medical data show that the accuracy is improved by 10% compared to traditional method, and the time is saved by 18% compared to traditional method.

Hyperspectral imaging is a rising imaging modality in the field of medical applications, and the combination of both spectral and spatial information provides wealth information for cell classification. In this paper, deep convolutional neural network (CNN) is employed to achieve blood cell discrimination in medical hyperspectral images (MHSI). As a deep learning architecture, CNNs are expected to get more discriminative and semantic features, which effect classification accuracy to a certain extent. Experimental results based on two real medical hyperspectral image data sets demonstrate that cell classification using CNNs is effective. In addition, compared to traditional support vector machine (SVM), the proposed method, which jointly exploits spatial and spectral features, can achieve better classification performance, showcasing the CNN-based methods' tremendous potential for accurate medical hyperspectral data classification.

Bone cancer is a significant health issue that leads to severe complications and deaths worldwide. Early detection can significantly improve patient outcomes, often resulting in a complete cure. Traditional approaches to managing bone cancer can be augmented with technology-driven methods, particularly those enabled by artificial intelligence. Convolutional Neural Networks (CNNs) and their variants have proven effective in medical data analysis. However, deep learning techniques require enhancements to improve performance in cancer detection across various medical imaging modalities. In this paper, we propose a deep-learning framework that utilizes advanced CNN models for the automatic screening of bone cancer. We have enhanced both the CNN model and the ResNet-50 model, which are integral components of the proposed framework. Additionally, we introduced an algorithm called Learning-based Bone Cancer Detection (LbBCD), designed to optimize the utilization of these enhanced deep learning models to improve bone cancer detection efficiency. Our research emphasizes a Region of Interest (ROI) based approach to enhance the screening process for bone cancer. Using a benchmark dataset known as the Bone CT Scan dataset, our empirical study demonstrated that the proposed deep learning framework, integrated with enhanced CNN and ResNet-50 models, achieved remarkable performance. Specifically, the enhanced CNN model reached an accuracy of 90.90%, while the enhanced ResNet-50 model achieved an accuracy of 92.20%, outperforming state-of-the-art deep learning models. Therefore, this proposed system can be integrated into healthcare applications for automatically screening bone cancer, contributing to a clinical decision support system for healthcare professionals.

This work has been supported by the EU H2020 grant Serums: Securing Medical Data in Smart Patient-Centric Healthcare Systems (code 826278).

It is of great significance to establish a scientific and perfect application model of medical and health big data analysis for promoting the application of medical and health big data. This paper mainly studies the computer-aided medical and health data analysis of Inner Mongolia Autonomous Region based on big data. This paper adopts literature research method, field research method and data science method. Literature research is used to understand big data platforms and their technologies, and to analyze the limitations of data analysis methods in traditional data warehouses. The application process of data analysis based on big data platform is studied by using data science method. The established medical and health data analysis application process based on the big data platform and the described medical and health big data application theme were consulted by using the expert consultation method, and they were modified and improved according to the expert opinions.KeywordsBig data platformComputer aidedHealthcare dataData analysis

Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many irrelevant, or redundant features. This work addresses the context-related feature selection problem from medical data by proposing utility of Sigma-if neural network being an effective model of neurology patients's low-level distributed selective attention mechanisms. Our experiments indicate that a context-aware technique can reduce the average cost of medical data acquisition and data processing as well as it can decrease classification error probability resulting in increasing the overall eHealth systems reliability.