Medical Decision Support System Research Articles

Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps

Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive approach for detection and treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) using Fuzzy Cognitive Maps (FCM). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Cognitive Maps and is intended to diagnose CAD utilizing specific inputs related to the patient’s clinical conditions. We show that the proposed model, when tested on a dataset collected from the Laboratory of Nuclear Medicine of the University Hospital of Patras achieves accuracy of 78.2% outmatching several state-of-the-art classification algorithms.

Curious Containers: A framework for computational reproducibility in life sciences with support for Deep Learning applications

In clinical scenarios, there is an increasing interest in complex computational experiments, as for example the training of Deep Learning models. Reproducibility is an essential property of such experiments, especially if the result contributes to a patient’s treatment. This paper introduces Curious Containers, a software framework for computational reproducibility that treats data, software and runtime environment as decentralized network resources. All experiment resources are described in a single file, using a new format that is compatible with a subset of the Common Workflow Language. Docker is used to deploy the experiment software in a container image, including arbitrary data transmission programs to connect with existing storage solutions. The framework supports Deep Learning applications, that have a high demand in storage and processing capabilities. Large datasets can be mounted inside containers via network filesystems like SSHFS based on the filesystem in user-space technology. The Nvidia-Container-Toolkit enables GPU usage. Curious Containers has been tested in two biomedical scenarios. The first use case is a Deep Learning application for tumor classification in images that requires a large dataset and a GPU. In this context, a prototypical integration of the framework with the existing Data Version Control system for exploratory Deep Learning modeling has been developed. The second use case extends an existing container image, including a scientific workflow for detection and comparison of human protein in mass spectrography data. The container image was originally developed for an archiving platform and could be extended to be compatible with both Curious Containers and cwltool, the Common Workflow Language reference implementation. The presented solution allows for consistent description and execution of computational experiments, while trying to be both flexible and interoperable with existing software and standards. Support for Deep Learning experiments is gaining importance as such systems are increasingly validated as medical decision support systems.

A Machine Learning-based Framework for Medical Decision Support Systems

Due to the complexity of clinical decision-making and the necessity for patient-specific suggestions, medical decision support systems (MDSS) are becoming more significant in healthcare. Machine learning (ML) can analyze and learn from vast volumes of patient data, making it a strong tool for MDSS development. Domain specialists, data scientists, and software engineers must work together to produce MDSS. This study provides a Machine Learning-based MDSS development approach that prioritizes stakeholder collaboration. Medical Decision Support Systems (MDSS) provide tailored recommendations based on clinical guidelines, medical expertise, and patient data to improve patient outcomes and healthcare delivery. Machine learning (ML) can analyze and learn from vast volumes of patient data, making it a strong tool for MDSS development. This study provides a Machine Learning-based MDSS development framework with data preparation, feature selection and extraction, model training and evaluation, and integration and deployment. To ensure MDSS accuracy and efficacy, domain experts, data scientists, and software engineers collaborate.

Sınıflandırma Algoritmalarını Kullanarak Meme Dokusunda Kitleleri Değerlendirmeye Yönelik Karar Destek Sistemi

Breast cancer is the most widely recognized cancer-related death among women globally. Epidemiological studies released in different parts of the world over the past two decades show a significant rise in mortality rates for breast cancer. Today, mammography is the most effective method for imaging masses and microcalcifications in breast tissue. On the other hand, breast biopsy predictions arising from mammogram analysis lead to nearly 70 percent biopsies of benign findings that can be prevented without a biopsy. An automated method is therefore required to assist physicians in mammography analysis prognoses. Researchers have suggested different medical decision support systems recently. In this study, a medical decision support system to be utilized in the process of a breast cancer diagnosis is proposed. The primary purpose of this system is to lower the number of unnecessary breast biopsies and make the diagnosis more reliable. Accordingly, apart from the age of the patient, BI-RADS assessment of the breast tissue, the shape of the mass, mass margin, tissue density, the class label indicating the severity of the lesion are evaluated using the performances of a Naive Bayes algorithm , which is a probabilistic classification algorithm, and Multilayer Perceptron algorithm, which is a feed forward neural network, as two different classification algorithms via a preferred dataset in which each mammography mass have six different feature. The proposed system can help to make a biopsy or short-time follow-up decision. The test results are promising that the proposed method can be used as a decision module in computer-aided diagnosis systems.

An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction

AbstractAt present days, Internet of Things (IoT) and cloud platforms become widely used in various healthcare applications. The enormous quantity of data produced by the IoT devices in the healthcare sector can be examined on the cloud platform instead of dependent on restricted storage and computation resources exist in the mobile gadgets. For offering effective medicinal services, in this article, an online medical decision support system (OMDSS) is introduced for chronic kidney disease (CKD) prediction. The presented model involves a set of stages namely data gathering, preprocessing, and classification of medical data for the prediction of CKD. For classification, logistic regression (LR) model is applied for classifying the data instances into CKD and non‐CKD. In addition, for tuning the parameters of LR, Adaptive Moment Estimation (Adam), and adaptive learning rate optimization algorithm is applied. The performance of the introduced model is examined using a benchmark CKD dataset. The experimental outcome observed the superior characteristics of the presented model on the applied dataset.

RETRACTED ARTICLE: Multilevel Tetrolet transform based breast cancer classifier and diagnosis system for healthcare applications

Breast cancer is a deadly disease and nowadays affects middle-aged women. Doctors use various methods to find out the presence of micro calcifications. In general, images of digital mammogram are used for cancer diagnosis. But the precision that determines the presence of cancer requires multiple restrictions. An improved medical decision support system for the classification of mammogram images using Unsupervised Test Vector Optimization (UTVO) based classifier is developed for health care and is presented in this work. The median filtering in the preprocessing stage removes the speckle, salt and pepper noises for mammogram image enhancement and the multilevel Tetrolet transformation based Matrix vectors are obtained for feature extraction. Multilevel Tetrolet transformation based features (MTF) are used for the selection of texture features. The UTVO classification approach proposed for identifying breast cancer exploits the potential and analyzes its performance after classification of benign and malignant cases. Finally, the UTVO classification system’s memory and computational time has been used to weigh the optimal weight and used to analyze the performance without compromise. The proposed system is expected to provide the best support for doctors to achieve maximum accuracy and improvement in health care.

Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.

Medical diagnosis decision support: online system

This project designs and implements an online medical diagnosis decision support system. The challenges faced by patients in accessing medical consultation, necessitated the developer to develop a user-friendly system to tackle these challenges. The system provides support on diagnosis decisions for the medically inclined. The system allows the study of the influence of the various symptoms that affect diagnosis. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various diseases. An exhaustive evaluation of the system by comparison with published cases is presented in this project. The overall result shows that the system is functional and reliable. The system was developed using Nodejs and MYSQL technologies implementing fuzzy logic.Keywords: Patience, Diagnosis, Preponderance, Practitioner, Fuzzy InferenceVol. 26 No 1 June 2019

The Effect of Copper Aided Nickel–Titanium Arch-Wire Sequences on Upper Jaw Expansion: A Comparison with Conventional Nickel–Titanium Wire Systems in Medical Decision Support Systems in Medical Internet of Things

The Effect of Copper Aided Nickel–Titanium Arch-Wire Sequences on Upper Jaw Expansion: A Comparison with Conventional Nickel–Titanium Wire Systems in Medical Decision Support Systems in Medical Internet of Things

An efficient healthcare framework for kidney disease using hybrid harmony search algorithm

Prediction of kidney disease (KD) gains more importance in the medical decision support systems. As the medical dataset are massive in size, effective techniques are required to produce accurate results. This paper proposes a hybrid harmony search (HM-L) algorithm with Levi distribution to properly predict KD at appropriate time. In this research work, correlation-based feature selection (CFS) is used as a feature selection technique. The effectiveness of hybrid harmony search (HS) algorithm is validated by employing it against a set of dataset. The obtained results of applied datasets without and with feature selection are compared to one another. The experimental results imply that the HM-L algorithm attains significant results than existing methods such as HS algorithm, biogeography optimisation algorithm (BBO), grey wolf optimisation (GWO), AL particle swarm optimisation algorithm (ALPSO) and artificial bee colony (ABC) algorithm. The presented HM-L model attains a sensitivity of 96, specificity of 93.33, accuracy of 95, F-score of 96 and kappa value of 0.89.

A cloud-based secured framework for smart medical diagnosis: a survey

Enormous amounts of medical data are being collected by several well-developed hospital information systems (HIS) in the form of patient records in hospitals. The hidden patterns and relationships contained in this data are identified using various data mining techniques which have drawn increasing world-wide attention in the recent years. As a result of which, a good number of medical decision support systems (MDSS) have been developed. These are computer systems designed to assist physicians or other healthcare professionals in making clinical decisions for the given patient's symptoms and medical history. Inspired by the existing systems, the model proposed in this paper aims at making a knowledge sharing collaborative platform for doctors which serves as a realistic and effective medical decision support system. It is important because it provides vital information from different data sources. It has several challenges like scalability, response time, heterogeneous data formats. This paper does a detailed study of different challenges in understanding about medical information that can be provided to users in a better manner. A framework named intelligent healthcare framework (IHCF) is being proposed in this paper detailing the services and support to society.

Алгоритм формирования подозрения на новую коронавирусную ин- фекцию на основе анализа симптомов для использования в системах поддержки принятия врачебных решений

The course of the COVID‑19 pandemic imposes a significant burden on healthcare systems, including on primary care, when it is necessary to correctly suspect and determine further management. The symptoms non-specificity and the manifestations versatility of the COVID‑19 impose difficulties in identifying suspicions. To improve the definition of COVID‑19 symptom checkers and medical decision support systems (MDSS) can potentially be useful. They can give recommendations for determining the disease management. The scientific analysis shows the manifestations versatility and the occurrence frequency COVID‑19. We structured the manifestations by occurrence frequency, classified them as “large” and “small”. The rules for their interaction were determined to calculate the level of suspicion for COVID‑19. Recommendations on patient management tactics were developed for each level of suspicion. NLP models were trained to identify the symptoms of COVID‑19 in the unstructured texts of electronic health records. The accuracy of the models on the F-measure metric ranged from 84.6% to 96.0%. Thus, a COVID‑19 prediction method was developed, which can be used in symptom checkers and MDSS to help doctors determine COVID‑19 and support tactical actions.

Medical decision support systems on the basis of artificial intelligence — strategy for the development of personalized medicine of the next stage

Prospects of creating decision support systems (DSS) in health care are substantiated in the article, the feasibility of mass implementation of DSS and the principle of their work on the basis of a medical and digital system for managing human capital are analyzed, examples of clinical DSS in Russia and abroad on the basis of 4P medicine are provided.

AN EFFICIENT HEALTHCARE FRAMEWORK FOR KIDNEY DISEASE USING HYBRID HARMONY SEARCH ALGORITHM

Prediction of kidney disease (KD) gains more importance in the medical decision support systems. As the medical dataset are massive in size, effective techniques are required to produce accurate results. This paper proposes a hybrid harmony search (HM-L) algorithm with Levi distribution to properly predict KD at appropriate time. In this research work, correlation-based feature selection (CFS) is used as a feature selection technique. The effectiveness of hybrid harmony search (HS) algorithm is validated by employing it against a set of dataset. The obtained results of applied datasets without and with feature selection are compared to one another. The experimental results imply that the HM-L algorithm attains significant results than existing methods such as HS algorithm, biogeography optimisation algorithm (BBO), grey wolf optimisation (GWO), AL particle swarm optimisation algorithm (ALPSO) and artificial bee colony (ABC) algorithm. The presented HM-L model attains a sensitivity of 96, specificity of 93.33, accuracy of 95, F-score of 96 and kappa value of 0.89.

Construction of Liver Fibrosis Diagnosis Ontology From Fuzzy Extended ER Modeling

Liver fibrosis diagnoses is a critical and core research study field due to its importance to the patient's life. Moreover, electronic health records (EHR) contain wealthy semantics connected to liver diseases yet ontological implementation is still a challenge. Ontology however, can play critical roles in E-health as a formalization of medical terminologies and decision support system knowledge base. But since clinical data contains a lot of data that is imprecise and vague, classical approaches of ontology construction would not be fruitful. However, Fuzzy ontology, an extension of the crisp ontology that requires different development methodology, can be implemented in this field due to its previous success in modeling semantic knowledge in various domains. In this article, the authors construct a fuzzy ontology by using a fuzzy extended entity relationship (EER) data model for liver fibrosis diagnosis. The resulting ontology is complete and consistent because it is based on a formal methodology of mapping the EER model into a fuzzy ontology.

Использование сверточной нейронной сети для автоматизации построения двумерных твердотельных моделей позвонков

Biomechanical modeling requires the construction of an accurate solid model of the object under study based on the data of a particular patient. This problem can be solved manually using modern software packages for medical data processing or using computer-aided design systems. This approach is used by many researchers and allows you to create accurate solid models, but is time consuming. In this regard, the automation of the construction of solid models suitable for performing biomechanical calculations is an urgent task and can be carried out using neural network technologies. This study presents the implementation of one of the methods for processing computed tomography data in order to create two-dimensional accurate solid models of vertebral bodies in a sagittal projection. An artificial neural network Mask-RCNN was used for automatic recognition of vertebrae. The assessment of the quality of the automatic recognition performed by the neural network was carried out on the basis of comparison with the S¨ orensen measure with manual segmentation performed by practitioners. Application of the method makes it possible to significantly speed up the process of modeling bone structures of the spine in 2D mode. The implemented technique was used in the development of a solid-state model module, which is included in the SmartPlan Ortho 2D medical decision support system developed at Saratov State University within the framework of the Advanced Research Foundation project.

Towards a Proposal of Personalized Medical Decision Support Systems: Analysis of Gene Expression Levels of Diabetes Mellitus, Inflammation and Oxidative Stress in Alzheimer’s Disease

Towards a Proposal of Personalized Medical Decision Support Systems: Analysis of Gene Expression Levels of Diabetes Mellitus, Inflammation and Oxidative Stress in Alzheimer’s Disease

ПРОЕКТИРОВАНИЕ И РЕАЛИЗАЦИЯ ГЕНЕРАТОРА СИГНАЛА ЭЛЕКТРОКАРДИОГРАММЫ(ЭКГ) НА БАЗЕ МИКРОКОНТРОЛЛЕРОВ СЕМЕЙСТВА STM32

The first Einthoven record on an electrocardiogram (ECG), which was launched in 1904.The paper deals with the problem of developing a generator of reference signals of electro-cardiography based on STM32 microcontrollers. The objectives of the study is to prepare a math-ematical description of the signals based on the Matlab package for the subsequent sending ofthese signals to the microcontroller Board. A hardware solution is proposed to create a program-mable multichannel electrocardiograph signal generator using only a programmable logic matrix based on STM32 microcontrollers and a small number of passive external components. A mathe-matical model of generating an artificial electrocardiogram and its application in a programma-ble simulator of signals of complex shape. When constructing medical decision support systems, computer tools are often used to provide not only the registration of biological signals, but also a quantitative assessment of certain parameters of these signals. For example, modern digital elec-trocardiographs allow you to automatically measure the amplitude-time characteristics of individ-ual elements of the electrocardiogram (ECG), which carry diagnostic information about the pro-cesses of excitation and relaxation of areas of the heart muscle. The use of an ECG generator has several advantages: reducing test time, simplifying the use of ECG signals without the need for expensive methods.

Создание информационной системы поддержки принятия врачебных решений на основе методов доказательной медицины

Создание информационной системы поддержки принятия врачебных решений на основе методов доказательной медицины

Application of Computer-Assisted Technologies to Optimization of Medical Care Measures in Piloted Space Exploration Missions

The system of medical care used in the Russian segment of the International Space Station in space missions is directed to support space crews in orbital flights. The system gives the priority to the telemedicine rather than medical procedures performed by the crew autonomously. The long-term experience showed effectiveness of the existing system for medical support of orbital flights; however, travels beyond the low-Earth’s orbit add substantial limitations calling for reconsideration of approaches to the crew medical care. Among the special features of these missions will be increasing time of autonomy. There is a high probability that crews will have to make medical decisions by themselves and computer-assisted systems are likely to be a proper tool of health care. The software of medical inventory control and practice guidance developed in the Institute for Biomedical Problems (IBMP) enhances quality of the day-to-day use of medical resources at the Russian Orbital Segment and can serve as a prototype of a medical decision support system within the intelligent circuit aboard future piloted spacecraft.