Medical Diagnosis Data Research Articles

Information Technology Support for Athlete Health Monitoring and Medical Diagnosis

The paper uses WSN to monitor athletes’ medical diagnosis information remotely. The aim is to strengthen the monitoring and management of athletes’ medical diagnosis data. The method of wireless sensor node and IPv6 routing based on IPv6 is proposed. The terminal-to-terminal network structure based on IP can carry long-distance transmission and share athlete medical diagnosis information. The monitoring system’s bottom structure design is realized using Contiki technology. At the same time, it also realizes the data acquisition and management in the monitoring system. The component interface is designed based on TinyOS. The design of the wireless client based on GPSR is completed based on GPSR. This paper uses the VanetMobiSim platform to complete a remote monitoring system’s software development and simulation test based on wireless medical diagnosis information. The results show that this system’s medical diagnosis information covers a wide range, and the information search efficiency is high.

An Improved Ant Colony Clustering Algorithm and Application in Classification of Tree Species

Abstract Clustering is one of the most challenging research topics and has attracted the attention of many researchers. Clustering aims to divide a given set of objects into several categories according to certain rules. It is widely applied to practical problems, such as knowledge discovery, artificial intelligence, scientific data analysis, business, biology, medical diagnosis, text mining, and web data mining and so on. Ant colony clustering algorithm (ACCA), which integrates ACA into clustering, is an efficient swarm intelligence algorithm. There are some weakness in the basic ant colony clustering algorithm, such as low solving efficiency, poor calculation accuracy and iteration stagnation. In this article, an improved Ant Colony Clustering Algorithm (IACCA), in which novel picking-up and dropping strategy is introduced, is proposed and applied to classification of tree species. The experiments indicate efficiency of IACCA for classification of tree species.

Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.

A blockchain based federated deep learning model for secured data transmission in healthcare Iot networks

The wide use of sensors in healthcare applications has made it necessary to have secure communication in healthcare Internet of Things (IoT) networks. The sensor data is sensitive, and can contain extremely confidential information such as medical diagnosis, clinical records, vital signs and health data of patients. The emergence of blockchain as a technology ensures consensus and trust among systems, and is now considered to be a new trend used to achieve high scalability, data integrity and privacy. Federated learning is a new technology based on distributed learning that exploits the concept of trust. In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries in an IoT network. This model is called the Blockchain enabled Federated Learning model for secured communication in healthcare IoT (BFL-hIoT), to secure data in healthcare IoT networks. This model is trained and tested on a standard dataset and demonstrates the highest classification accuracy of 97.16 % for normal, 0.9546 for backdoors, 0.9618 for XSS etc., outperforming other blockchain and deep learning models.

Design and Optimization of a Wearable Underactuated Mechanism for Spinal Posture Measurement

Abstract This paper proposes a novel wearable device to monitor and record the posture and alignment of spine. The proposed device adopts an underactuated mechanism design which allows it to adapt to the multiple-degrees-of-freedom spinal posture with minimum weight and complexity. To ensure the validity of measurement and comfort of wearing, the mechanism parameters are determined first by considering a special posture and then are fine-tuned using an optimization algorithm so that uniform contact forces for several selected spinal postures can be achieved. Experiments demonstrate that the device can automatically maintain contact with the wearer’s back and offer real-time spinal posture and alignment data for medical diagnosis and treatment.

Developing and Analyzing Deep Learning and Natural Language Processing Systems in the Context of Medical Information Processing

This work aims to develop and analyze deep learning and natural language processing systems in the context of medical information processing. The amount of data created about patients in the healthcare system is always increasing. The human review of this enormous volume of data derived from numerous sources is expensive and takes a lot of time. Additionally, during a patient visit, doctors write down the patient’s medical encounter and send it to nurses and other medical departments for processing. Often, the doctor doesn’t have enough time to record every observation made while examining the patient and asking about their medical history which takes time for a medical diagnosis to be made. The manual review of this vast amount of data generated from multiple sources is costly and very time-consuming. It brings huge challenges while attempting to review this data meaningfully. Therefore, the goal of this research is to create a system that will address the aforementioned issues. The suggested method extracts voice data from medical encounters and converts it to text using Deep Learning (DL) and Natural Language Processing (NLP) techniques. More so, the system developed will improve medical intelligence processing by using deep learning to analyze medical datasets and produce results of a diagnosis, assisting medical professionals at various levels in making realistic, intelligent decisions in real-time regarding crucial health issues. The system was designed using the Object-Oriented Analysis and Design Methodology (OOADM), and the user interfaces were put into place utilizing Natural Language Processing techniques, particularly speech recognition and natural language comprehension. Speech recognition allows for the taking of free text notes, which can drastically cut down on the amount of time medical staff spends on labor-in the tensive clinical recording. By extracting different pieces of data for medical diagnosis and producing results in a matter of seconds, a deep learning algorithm demonstrates a significant capacity to construct clinical decision support systems. The system’s results demonstrate that the deep learning algorithm enabled medical intelligence to be 96.7 percent accurate.

Utilizing the Clustering Techniques using Distance-Based Similarity Measures of SVNS in Medical Diagnosis

The clustering techniques, combined with distance-based similarity measures of Single Valued Neutrosophic Sets (SVNS) are studied and applied in medical diagnosis. The study starts with reviewing SVNS' theoretical foundations, emphasising its ability to capture and handle ambiguous data. This study focuses on integrating distance-based similarity measurements to improve the clustering process, which has seen limited implementation thus far. The set of data includes three patients with five symptoms and three diagnoses. To deal with the data in medical diagnosis, each patient is diagnosed with a disease based on distance-based similarity measures. The disease with the highest similarity measure value indicates the recognized disease for that patient. Then, the diseases are clustered into different categories depend on the values of confidence level. The obtained results show that the suggested method enhances the precision of medical diagnosis significantly, especially in cases with ambiguity and uncertainty.

Dynamic modeling of photoacoustic sensor data to classify human blood samples.

The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios.

Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe.

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners' unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.

Improving the Trustworthiness of Interactive Visualization Tools for Healthcare Data through a Medical Fuzzy Expert System.

Successful healthcare companies and illness diagnostics require data visualization. Healthcare and medical data analysis are needed to use compound information. Professionals often gather, evaluate, and monitor medical data to gauge risk, performance capability, tiredness, and adaptation to a medical diagnosis. Medical diagnosis data come from EMRs, software systems, hospital administration systems, laboratories, IoT devices, and billing and coding software. Interactive diagnosis data visualization tools enable healthcare professionals to identify trends and interpret data analytics results. Selecting the most trustworthy interactive visualization tool or application is crucial for the reliability of medical diagnosis data. Thus, this study examined the trustworthiness of interactive visualization tools for healthcare data analytics and medical diagnosis. The present study uses a scientific approach for evaluating the trustworthiness of interactive visualization tools for healthcare and medical diagnosis data and provides a novel idea and path for future healthcare experts. Our goal in this research was to make an idealness assessment of the trustworthiness impact of interactive visualization models under fuzzy conditions by using a medical fuzzy expert system based on an analytical network process and technique for ordering preference by similarity to ideal solutions. To eliminate the ambiguities that arose due to the multiple opinions of these experts and to externalize and organize information about the selection context of the interactive visualization models, the study used the proposed hybrid decision model. According to the results achieved through trustworthiness assessments of different visualization tools, BoldBI was found to be the most prioritized and trustworthy visualization tool among other alternatives. The suggested study would aid healthcare and medical professionals in interactive data visualization in identifying, selecting, prioritizing, and evaluating useful and trustworthy visualization-related characteristics, thereby leading to more accurate medical diagnosis profiles.

PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification.

Imbalanced training data in medical image diagnosis is a significant challenge for diagnosing rare diseases. For this purpose, we propose a novel two-stage Progressive Class-Center Triplet (PCCT) framework to overcome the class imbalance issue. In the first stage, PCCT designs a class-balanced triplet loss to coarsely separate distributions of different classes. Triplets are sampled equally for each class at each training iteration, which alleviates the imbalanced data issue and lays solid foundation for the successive stage. In the second stage, PCCT further designs a class-center involved triplet strategy to enable a more compact distribution for each class. The positive and negative samples in each triplet are replaced by their corresponding class centers, which prompts compact class representations and benefits training stability. The idea of class-center involved loss can be extended to the pair-wise ranking loss and the quadruplet loss, which demonstrates the generalization of the proposed framework. Extensive experiments support that the PCCT framework works effectively for medical image classification with imbalanced training images. On four challenging class-imbalanced datasets (two skin datasets Skin7 and Skin 198, one chest X-ray dataset ChestXray-COVID, and one eye dataset Kaggle EyePACs), the proposed approach respectively obtains the mean F1 score 86.20, 65.20, 91.32, and 87.18 over all classes and 81.40, 63.87, 82.62, and 79.09 for rare classes, achieving state-of-the-art performance and outperforming the widely used methods for the class imbalance issue.

An adaptive loss backward feature elimination method for class-imbalanced and mixed-type data in medical diagnosis

Feature selection or elimination methods have been widely used to mine high-dimensional data from many fields such as biomedicine. However, classical feature selection methods usually focus on class-balanced and numerical data, and the classification performance is poor when processing class-imbalanced or mixed-type data. Moreover, existing studies hardly consider the co-occurrence of both class-imbalance and mixture of numerical and nominal features. In this paper, we propose an adaptive loss backward feature elimination method for class-imbalanced and mixed-type data (FEIM). In FEIM, a technique to convert and decompose nominal features is employed to process mixed-type data. Meanwhile, an adaptive loss function is proposed to make the classifier pay more attention to the minority class. In experiments, the effectiveness and stability of FEIM are synthetically evaluated against seven medical diagnostic datasets in terms of the measurements of F measurement and precision, and the results of feature selection are analyzed in detail. The experimental results show that, compared with three common methods (Recursive Feature Elimination SVM (RFE-SVM), Relief, and Holdout Algorithm for Backward Feature Elimination (HO-BFE)), FEIM can achieve a higher classification accuracy and is more stable when the parameters are changed. On the other hand, the feature selection results of FEIM are more meaningful by processing class-imbalanced and mixed-type data. Therefore, FEIM is a competitive and effective method to process feature elimination for class-imbalanced and mixed-type biomedical data for in medical diagnosis. The supporting information is available in the electronic supplementary material.

A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID‐19 patients

AbstractCurrently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID‐19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource‐limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication

Noninvasive ultrasound assessment of tissue internal pressure using dual mode elasticity imaging: a phantom study

Objective. Tissue internal pressure, such as interstitial fluid pressure in solid tumors and intramuscular pressure in compartment syndrome, is closely related to the pathological state of tissues. It is of great diagnostic value to measure and/or monitor the internal pressure of targeted tissues. Because most of the current methods for measuring tissue pressure are invasive, noninvasive methods are highly desired. In this study, we developed a noninvasive method for qualitative assessment of tissue internal pressure based on a combination of two ultrasound elasticity imaging methods: strain imaging and shear wave elasticity imaging. Approach. The method was verified through experimental investigation using two tissue-mimicking phantoms each having an inclusion confined by a membrane, in which hydrostatic pressures can be applied and maintained. To examine the sensitivity of the elasticity imaging methods to pressure variation, strain ratio and shear modulus ratio (SMR) between the inclusion and background of phantom were obtained. Main results. The results first experimentally prove that pressure, in addition to elasticity, is a contrast mechanism of strain imaging, and further demonstrate that a comparative analysis of strain ratio and SMR is an effective method for noninvasive tissue internal pressure detection. Significance. This work provides a new perspective in interpreting the strain ratio data in medical diagnosis, and it also provides a noninvasive alternative for assessing tissue internal pressure, which could be valuable for the diagnosis of pressure-related diseases.

Weighted fuzzy rough sets-based tri-training and its application to medical diagnosis

The theory of fuzzy rough sets is an effective soft computing paradigm for dealing with vague, uncertain, or imprecise data. However, most existing fuzzy rough sets-based methods may suffer from robustness since all samples are considered equally and also these methods are designed to cater for supervised or unsupervised learning. In this paper, we propose a weighted fuzzy rough sets-based multi-view tri-training model for partially labeled data. Specifically, considering the negative effect of noise, we first use a technique of data editing to filter potentially possible noises, and then a gradient descent algorithm is employed to optimize the weight of each sample with the objective of maximizing high-order weighted fuzzy dependency, based on which a robust weighted fuzzy rough set model is developed for labeled data. Moreover, we introduce the robust weighted fuzzy rough sets into tri-training and propose multi-view-based robust tri-training for partially labeled data by exploring data representations in the original view, the transformed view of principal component analysis, and the granular view after discretization. Extensive experiments conducted on UCI benchmark and medical diagnosis data sets show that the proposed model achieves favorable results in both supervised and semi-supervised scenarios.

Deducing health cues from biometric data

Medical diagnosis involves the expert opinion of trained health care professionals based on causal inference from medical data. While medical data are typically collected using specialized medical-grade sensors, similar data characteristics useful for medical diagnosis are sometimes present in biometric data (e.g., face images, ocular images, and speech signals). In this paper, we explore the biometrics and medical literature to study the following questions. 1) What kind of health cues are embedded in the commonly utilized forms of audio-visual biometric data? 2) How can these health cues be gleaned from the biometric data, and what kind of diseases can it help diagnose? 3) What are some of the implications of using biometric data for medical diagnosis?

Nearly Reversible ECG Steganography based on Polar Coordinate System with Digital Replacement Technique

In this paper, we present a nearly reversible data hiding for electrocardiogram (ECG) hosts. Based on the polar coordinate system domain, medical diagnosis and personal data can be embedded in an ECG signal by the simple digital replacement technique. Simulations revealed that the restored ECG with near lossless quality can be obtained by the proposed method at receiver site. In addition, the perceived quality of the marked ECG is very good with a high payload size. Moreover, the resultant signal-to-noise ratio (SNR), peak SNR, and payload of the proposed method outperforms those of existing techniques. Since the computation cost is low, the proposed method can be used in portable biometrics or ECG measuring instruments.

MACHINE LEARNING TECHNIQUES BASED ON FEATURE SELECTION FOR IMPROVING AUTISM DISEASE CLASSIFICATION

Nowadays, Autism Spectrum Disorder (ASD) is one of the primary psychiatric disorders illness that rapidly increases. One of the main problems of medical diagnosis data and classification is the variance in symptoms between patients. Thus, finding the discriminative symptoms that distinguish the illness accurately is an important issue. This paper will explore various feature selection methods on four ASD datasets for extracting significant features for improving the ASD classification system. Datasets were created in 2017 and 2018 for child and adult gathered online. Several feature engineering techniques are applied to rank significant features. The correlation matrix method showed the association between features that enable us to select the highest significant features. Then each dataset split into 70% for training and 30% for test. Several machine learning classifiers are applied. After testing, the selected features achieve 100% accuracy, specificity, sensitivity, AUC, and f1 score with adaboost, linear discriminant analysis and logistic regression classifier on different size of data. I choose the adaboost model because it does the same performance with less time and less computational power in both dataset 2017 and 2018 for child and adult. Results were validated using cross-validation with 10 k-fold. The code applied in that paper in https://github.com/BasmaRG/ASD/ .

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy

Cumin (Cuminum cyminum) and fennel (Foeniculum vulgare) are widely used seasonings and play a very important role in industries such as breeding, cosmetics, winemaking, drug discovery, and nano-synthetic materials. However, studies have shown that cumin and fennel from different regions not only differ greatly in the content of lipids, phenols and proteins but also the substances contained in their essential oils are also different. Therefore, realizing precise identification of cumin and fennel from different regions will greatly help in quality control, market fraud and production industrialization. In this experiment, cumin and fennel samples were collected from each region, a total of 480 NIR spectra were collected. We used deep learning and traditional machine learning algorithms combined with near infrared (NIR) spectroscopy to identify their origin. To obtain the model with the best generalization performance and classification accuracy, we used principal component analysis (PCA) to reduce spectral data dimensionality after Rubberband baseline correction, and then established classification models including quadratic discriminant analysis based on PCA (PCA-QDA) and multilayer perceptron based on PCA (PCA-MLP). We also directly input the spectral data after baseline correction into convolutional neural networks (CNN) and generative adversarial networks (GAN). The experimental results show that GAN is more accurate than the PCA-QDA, PCA-MLP and CNN models, and the classification accuracy reached 100%. In the cumin and fennel classification experiment in the same region, the four models achieve great classification results from three regions under the condition that all model parameters remain unchanged. The experimental results show that when the training data are limited and the dimension is high, the model obtained by GAN using competitive learning has more generalization ability and higher classification accuracy. It also provides a new method for solving the problem of limited training data in food research and medical diagnosis in the future.

Artificial intelligence using for medical diagnosis via implementation of expert systems

Modern biomedical technologies development affords to provide the doctor with colossal amount of information about patients organism condition. However, the opportunity of using this data for medical diagnosis fully now is a distantive perspective only. The reason is a humans limited ability in assessment and interpretation this data arrays. The solution seems in artificial intelligence and expert systems wide introduction to medicine. Currently, almost all authors consider various options for constructing artificial neural networks as a way to implement artificial intelligence. This approach, which goes back to the fundamental theorem of A.N. Kolmogorov, the works of V.I. Arnold and Hecht-Nielsen [3], demonstrates excellent capabilities in a number of pattern recognition problems, which are reduced to revealing hidden details against the background of input noises. Much less often is mentioned such a method of modeling formal thinking as expert systems, which arose in the 1960s and then went into the shadows. Since the inception of cybernetics, computer programmers have tried to reproduce the mechanism of human thinking, that is, the task was to teach the computer to "think". The first known results in the field of creating and using intelligent systems were laid by the work of Norbert Wiener and G.S. Altshuller. At the same time, the creation of intelligent systems was reduced to the development of programs that solve problems using a variety of heuristic methods based on the property of human thinking to generalize.