Medical Decision System Research Articles

Asymptotic multilayer pooled transformer based strategy for medical assistance in developing countries

With the continuous progress of medical imaging technology, pathology images have become an important basis for doctors to judge the condition. However, pathology images have problems such as large numbers, large sizes, and complex backgrounds, which make the task of manual recognition exceptionally difficult. The use of computer-aided diagnosis technology can improve the diagnostic accuracy of medical image recognition. Transformer model-based methods have made great progress in the field of medical image recognition, but the self-attention mechanism in them has high computational complexity. Although single-layer pooling can reduce the computational cost, the strategy is prone to feature loss, and too many self-attention operations can exacerbate the distraction problem. Based on this, this study proposes an asymptotic multilayer pooling transformer-based pathology image assistance strategy in medical decision-making systems. First, we pre-process the pathology images with denoising and enhancement to accurately capture the detailed features of the lesion region. Then the asymptotic multilayer pooling transformer pyramid structure (PMPSNet) is used to identify cell nuclei. In the self-focused module, a multilayer pooling operation is employed to simplify the sequence and efficiently capture contextual features. In addition, the strategy utilizes a pyramid encoder to obtain multi-scale feature maps and a novel multilayer perceptual structure to limit the attention interference problem. The results show that our proposed PMPSNet-L model achieves a DSC value of 0.822, which is about 1.4 % better than the second-ranked model, and the IoU value reaches up to 0.702. In addition, our PMPSNet-S maintains a relatively lightweight parameter size of about 14.49 million, which is considerably lower than the existing segmentation models. Our proposed method not only achieves higher segmentation accuracy but also has fewer model parameters and lower computational complexity.

A novel deer hunting optimized cat boost approach for interactive medical decision-making system

The need for precise and effective medical decision-making mechanisms is driving constant change in the healthcare sector. In this regard, algorithms based on machine learning are becoming prominent due to their potential to help medical practitioners diagnose and cure a wide range of illnesses. All over the world, one significant public health issue is chronic kidney disease (CKD), which requires prompt and precise diagnosis to manage and cure. A new Deer Hunting Optimized CatBoost (DHO-CB) method for CKD seriousness prediction is presented in this research. Our approach takes advantage of CatBoost's built-in flexibility and resilience by incorporating an optimization framework inspired by deer hunting. This framework dynamically modifies model parameters to traverse intricate medical data environments. The standard CKD dataset is used to verify the suggested DHO-CB approach, and Python is the primary language used in this study. In terms of kappa coefficient (92.6%), f1-score metrics (97.7%), accuracy (97.2%), and specificity (95.5%), the effectiveness of the suggested DHO-CB approach is examined. Based on this analyzed data, our suggested DHO-CB approach outperforms other current approaches in CKD prediction as well as severity assessment. The suggested DHO-CB offers a viable path toward the creation of complex yet approachable medical decision support systems, which will promote increased accuracy and effectiveness in the identification and treatment of CKDs.

Medical-informed machine learning: integrating prior knowledge into medical decision systems

BackgroundClinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML.MethodsThe study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models.ResultsThe findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios.ConclusionsBy illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

Continuous Refinement-based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems.

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial information more quickly. The cascade structure refines the segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling and cropping of patches during the inference process due to the ultra-high resolution and complex structure of pathology images. This not only increases the cost and computation time but also results in the loss of cellular details and corrupts the global contextual information. This study proposes a Digital Pathology Image Assistance Program (CRSDPI) for medical decision-making systems that is based on continuous improvement. After locating the region of interest using the maximum inter-class variance method, the pictures are preprocessed to account for the impacts of staining inconsistencies and sensitivity variations on the model's performance. Ultimately, we create a two-phase continuously refined segmentation network (TCRNet) by combining an enhanced continuous refinement model with a coarse segmentation network built on a pyramid scene parsing network. The coarse segmentation network introduces an auxiliary loss term to speed up convergence, and the refined model introduces an implicit function to reduce computational cost and reconstruct more details. The TCRNet model refines the target by successively aligning the features without the need to take cascading decoder operations after encoder. Experiments conducted on digital pathology images of breast cancer and osteosarcoma demonstrate the superior prediction accuracy and computational speed of our strategy.

Cytopathology image analysis method based on high-resolution medical representation learning in medical decision-making system

Artificial intelligence has made substantial progress in many medical application scenarios. The quantity and complexity of pathology images are enormous, but conventional visual screening techniques are labor-intensive, time-consuming, and subject to some degree of subjectivity. Complex pathological data can be converted into mineable image features using artificial intelligence image analysis technology, enabling medical professionals to quickly and quantitatively identify regions of interest and extract information about cellular tissue. In this study, we designed a medical information assistance system for segmenting pathology images and quantifying statistical results, including data enhancement, cell nucleus segmentation, model tumor, and quantitative analysis. In cell nucleus segmentation, to address the problem of uneven healthcare resources, we designed a high-precision teacher model (HRMED_T) and a lightweight student model (HRMED_S). The HRMED_T model is based on visual Transformer and high-resolution representation learning. It achieves accurate segmentation by parallel low-resolution convolution and high-scaled image iterative fusion, while also maintaining the high-resolution representation. The HRMED_S model is based on the Channel-wise Knowledge Distillation approach to simplify the structure, achieve faster convergence, and refine the segmentation results by using conditional random fields instead of fully connected structures. The experimental results show that our system has better performance than other methods. The Intersection over the Union (IoU) of HRMED_T model reaches 0.756. The IoU of HRMED_S model also reaches 0.710 and params is only 3.99 M.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble.

In the domain of AI-driven healthcare, deep learning models have markedly advanced pneumonia diagnosis through X-ray image analysis, thus indicating a significant stride in the efficacy of medical decision systems. This paper presents a novel approach utilizing a deep convolutional neural network that effectively amalgamates the strengths of EfficientNetB0 and DenseNet121, and it is enhanced by a suite of attention mechanisms for refined pneumonia image classification. Leveraging pre-trained models, our network employs multi-head, self-attention modules for meticulous feature extraction from X-ray images. The model's integration and processing efficiency are further augmented by a channel-attention-based feature fusion strategy, one that is complemented by a residual block and an attention-augmented feature enhancement and dynamic pooling strategy. Our used dataset, which comprises a comprehensive collection of chest X-ray images, represents both healthy individuals and those affected by pneumonia, and it serves as the foundation for this research. This study delves deep into the algorithms, architectural details, and operational intricacies of the proposed model. The empirical outcomes of our model are noteworthy, with an exceptional performance marked by an accuracy of 95.19%, a precision of 98.38%, a recall of 93.84%, an F1 score of 96.06%, a specificity of 97.43%, and an AUC of 0.9564 on the test dataset. These results not only affirm the model's high diagnostic accuracy, but also highlight its promising potential for real-world clinical deployment.

Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images.

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.

Diagnostics of retinal pathologies by optical coherence tomography images using artificial intelligence tools

The importance of early detection and monitoring of retinal diseases determines the relevance of the study devoted to the diagnosis of retinal pathologies by OCT images using artificial intelligence (AI) tools.The purpose is to develop algorithms for diagnosing retinal pathologies from OCT images by machine learning methods.Material and methods. The study used a dataset (20,000 eyes), publicly available on the Internet, which contains OCT images of healthy retina (5,000 eyes) and retina affected by three different pathologies (choroid neovascularization, macular edema, multiple drusen, 15,000 eyes). The retinal pathology recognition system is based on a trained neural network VGG16 (developed by a visual geometry group of Oxford University).Results. The main result of the research is the development of an algorithm, implemented on Python, for the diagnosis of retinal diseases from OCT images based on convolutional neural network AI tool. The sensitivity and selectiveness of the neural network model during the diagnosis of retinal diseases were 97 and 98%, respectively.Conclusion. AI methods used in the retinal pathology automatic detection system developed at the Helmholtz National Medical Research Center of Eye Diseases as part of automated medical decision-making system have been shown to have high potential and efficiency. In the future, this service can be used to improve the effectiveness of early diagnosis and monitoring of retinal diseases in conditions of reduced availability of primary ophthalmological care in some of the territories of the Russian Federation, including that provided at the pre-doctoral stage.

Two‐stage coarse‐to‐fine method for pathological images in medical decision‐making systems

AbstractArtificial intelligence decision systems play an important supporting role in the field of medical information. Medical image analysis is an important part of decision systems and an even more important part of medical diagnosis and treatment. The wealth of cellular information in histopathological images makes them a reliable means of diagnosing tumors. However, due to the large size, high resolution, and complex background structure of pathology images, deep learning methods still have various difficulties in the recognition of pathology images. Based on this, this study proposes a two‐stage continuous improvement‐based approach for pathology image recognition in medical decision systems. For pathology images with complex backgrounds, normalization and enhancement is performed to remove the effects of noise color, and light‐dark inconsistencies on the segmentation network. The continuous refinement PSP Net (CRPSPNet) is then designed for accurate recognition of the pathology images. CRPSPNet is divided into two stages: Pyramid Scene Parsing Network segmentation to obtain coarse segmentation results; and continuous refinement model refines the results of the first stage. Experiments using more than 1,000 osteosarcoma pathology images have shown that the method gives more accurate results with fewer computer resources and processing time than traditional optimization models. Its Intersection over Union achieves 0.76.

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models

BackgroundHepatitis C is a prevalent disease that poses a high risk to the human liver. Early diagnosis of hepatitis C is crucial for treatment and prognosis. Therefore, developing an effective medical decision system is essential. In recent years, many computational methods have been proposed to identify hepatitis C patients. Although existing hepatitis prediction models have achieved good results in terms of accuracy, most of them are black-box models and cannot gain the trust of doctors and patients in clinical practice. As a result, this study aims to use various Machine Learning (ML) models to predict whether a patient has hepatitis C, while also using explainable models to elucidate the prediction process of the ML models, thus making the prediction process more transparent.ResultWe conducted a study on the prediction of hepatitis C based on serological testing and provided comprehensive explanations for the prediction process. Throughout the experiment, we modeled the benchmark dataset, and evaluated model performance using fivefold cross-validation and independent testing experiments. After evaluating three types of black-box machine learning models, Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, we adopted Bayesian-optimized RF as the classification algorithm. In terms of model interpretation, in addition to using common SHapley Additive exPlanations (SHAP) to provide global explanations for the model, we also utilized the Local Interpretable Model-Agnostic Explanations with stability (LIME_stabilitly) to provide local explanations for the model.ConclusionBoth the fivefold cross-validation and independent testing show that our proposed method significantly outperforms the state-of-the-art method. IHCP maintains excellent model interpretability while obtaining excellent predictive performance. This helps uncover potential predictive patterns of the model and enables clinicians to better understand the model's decision-making process.

The telemedicine and digital technologies in education programs of training medical personnel of higher qualification

The article considers application of telemedicine and digital technologies in educational programs of training medical personnel in residency and graduate school for the Russian health care system. The possibilities of telemedicine and digital technologies that currently are in use for remote medical care, consultations of medical workers by qualified medical specialists and for training and re-training of medical personnel are investigated. The key topics of modern health care are improvement of system of training medical manpower, personalization of medical care in health care, individualization of professional orientation in the process of continuing medical education and current methods of information environment management in health care sector. The conclusion is made that owing to new technologies,such innovations as telemedicine, artificial intelligence, medical decision-making systems, remote platforms of patient health monitoring and other modern inventions are implemented into health care. The purpose of the study is to examine impact of transformational processes in higher medical education in epoch of digitization.

Development of Bioimpedance Spectroscopy Technology in Medical Decision Support Systems

The purpose of research – development of bioimpedance spectroscopy methods to develop on their basis objective and realistically accessible criteria for assessing the severity and prognosis of diseases, as well as evaluating the effectiveness of treatment methods, developing criteria for the use of conservative therapy options and surgical interventions in severe patients.Methods. The proposed method involves the use of a recurrent modified Voigt model as a biomaterial segment impedance model. For each model of a biomaterial segment, a Cole plot is plotted in a given frequency range. At the stage of determining the parameters of each of the models, a recurrent procedure is performed, which is the solution of systems of nonlinear equations, starting from one link of the Voight model with a subsequent increase in their number at each iteration step, until the value of the approximation error by the Voight model of the Cole experimental plot reaches allowed value.Results. As a result of the study, fundamentally new results have been obtained that allow creating intelligent decision support systems for diagnosing socially significant diseases. A bioimpedance analysis model based on multifrequency bioimpedance measurement has been created, which makes it possible to decompose the biomaterial impedance into structural elements, on the basis of which to determine descriptors for neural network classifiers of medical risk. In the work, an analysis of classifier errors was carried out in classifying the risk of acute destructive pancreatitis, which showed that the maximum value of the quality indicators of various classifier models was 78%, the minimum was 62%, demonstrating close values to the quality indicators of the ultrasound diagnostic method.Conclusion. The use of multifrequency sensing and modified Voight models in neural network classifiers of medical risk makes it possible to build clinical decision support systems for diagnosing socially significant diseases, as well as the ability to improve classification quality indicators and expand the functionality of intelligent medical decision-making systems.

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients

BackgroundA growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients.MethodsWe used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients.ResultsThe results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%.ConclusionsThe treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs.

A Novel Medical Decision-Making System Based on Multi-Scale Feature Enhancement for Small Samples

The medical decision-making system is an advanced system for patients that can assist doctors in their medical work. Osteosarcoma is a primary malignant tumor of the bone, due to its specificity, such as its blurred borders, diverse tumor morphology, and inconsistent scales. Diagnosis is quite difficult, especially for developing countries, where medical resources are inadequate per capita and there is a lack of professionals, and the time spent in the diagnosis process may lead to a gradual deterioration of the disease. To address these, we discuss an osteosarcoma-assisted diagnosis system (OSADS) based on small samples with multi-scale feature enhancement that can assist doctors in performing preliminary automatic segmentation of osteosarcoma and reduce the workload. We proposed a multi-scale feature enhancement network (MFENet) based on few-shot learning in OSADS. Global and local feature information is extracted to effectively segment the boundaries of osteosarcoma by feeding the images into MFENet. Simultaneously, a prior mask is introduced into the network to help it maintain a certain accuracy range when segmenting different shapes and sizes, saving computational costs. In the experiments, we used 5000 osteosarcoma MRI images provided by Monash University for testing. The experiments show that our proposed method achieves 93.1% accuracy and has the highest comprehensive evaluation index compared with other methods.

A Tumor MRI Image Segmentation Framework Based on Class-Correlation Pattern Aggregation in Medical Decision-Making System

Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption.

On probabilities of missed target and false alarm іn medical decision-making systems

Binary classifiers are widely used in solving a number of applied problems, in particular, problems of medical diagnostics. The effectiveness of such systems is characterized by target miss errors and false alarms. Since the probability of a false alarm error usually increases with a decrease in the probability of an allowance error, and vice versa, the tuning of systems implies a certain compromise between the indicated errors. To build a diagnostic algorithm under a priori uncertainty, the Neyman–Pearson strategy is often used, which involves minimizing the probability of a target miss error with a given constraint on the probability of a false alarm error. To study the question of the formal choice of an acceptable constraint, conditions for the usefulness of a diagnostic algorithm are formulated. On the basis of these conditions, the admissible limits of the probabilities of target miss errors and false alarms are determined. The boundary values of the prevalence of diseases are determined, at which a diagnostic algorithm with known operational characteristics remains useful in terms of reducing average losses. Examples are given to illustrate the practical value of the obtained conditions. The features of the decision-making algorithm based on medical symptoms are considered. It has been shown that if, according to medical statistics, a certain symptom is typical for most sick patients and uncharacteristic for most healthy people, but this does not mean high reliability of diagnostic decisions based on the analysis of this symptom in a particular person. Using the example of the analysis of the symptom «Characteristic skin rashes», it was demonstrated that even if the probability of the absence of a symptom in healthy people is 0,99, the probability of a false alarm may be lower than 0,01. This is explained by the fact that in order to make an substantiated decision for a particular patient, it is necessary to make sure that the indicated symptom is absent during a certain observation interval.

Computational method of the cardiovascular diseases classification based on a generalized nonlinear canonical decomposition of random sequences

Decision support systems can seriously help medical doctors in the diagnosis of different diseases, especially in complicated cases. This article is devoted to recognizing and diagnosing heart disease based on automatic computer processing of the electrocardiograms (ECG) of patients. In the general case, the change of the ECG parameters can be presented as a random sequence of the signals under processing. Developing new computational methods for such signal processing is an important research problem in creating efficient medical decision support systems. Authors consider the possibility of increasing the diagnostic accuracy of cardiovascular diseases by implementing of the new proposed computational method of information processing. This method is based on the generalized nonlinear canonical decomposition of a random sequence of the change of cardiogram parameters. The use of a nonlinear canonical model makes it possible to significantly simplify the maximum likelihood criterion for classifying diseases. This simplification is provided by the transition from a multi-dimensional distribution density of cardiogram parameters to a product of one-dimensional distribution densities of independent random coefficients of a nonlinear canonical decomposition. The absence of any restrictions on the class of random sequences under study makes it possible to achieve maximum accuracy in diagnosing cardiovascular diseases. Functional diagrams for implementing the proposed method reflecting the features of its application are presented. The quantitative parameters of the core of the computational diagnostic procedure can be determined in advance based on the preliminary statistical data of the ECGs for different heart diseases. That is why the developed method is quite simple in terms of computation (computing complexity, accuracy, computing time, etc.) and can be implemented in medical computer decision systems for monitoring cardiovascular diseases and for their diagnosis in real time. The results of the numerical experiment confirm the high accuracy of the developed method for classifying cardiovascular diseases.

Predicting outcomes in kidney stone endoscopic surgery by rotation forest algorithm

ABSTRACT Artificial intelligence (AI) has developed in medicine significantly during recent decades. Medical decision systems prevent probable errors stemming from clinical experts’ fatigue or inexperience. In addition, researchers can analyse medical data sets with the least time and the most details by the above-mentioned system. Nowadays, health care centres face mass data while extracting knowledge which is the most critical global challenges since diagnosis, cure, and prevention of diseases are considered extremely hard. Physicians should check patient test results and decisions made in the past for similar patients in order to decide properly due to the complexity of predicting the outcome of surgery. However, an automatic instrument such as data mining should be used for exploring patients due to their numbers. The present study utilised k-fold in front of the t-Test method to determine the best algorithm changing the parameters related to classification algorithms for increasing accuracy and found the best amount. Input variables included age, gender, Body Mass Index (BMI), previous surgery in target kidney, history of dialysis, one kidney, a kidney transplant, side of the kidney, stone side, size, and volume, postoperative stone volume, stone location, renal and skeletal anomaly, degree of hydronephrosis, stone lucency, history of high blood pressure and open nephrolithotomy, preoperative and postoperative haemoglobin and creatinine, Hx of hypertension and diabetes, heart disease, access point, radiation exposure, during access, total radiation, and operative time, while output variables included stone-free status and need for further surgeries such as Extracorporeal Shock Wave Lithotripsy (ESWL), Transurethral Lithotripsy (TUL), and Double J (DJ). Based on the results, the rotation forest algorithm and Area under the ROC Curve (AUC = 0.960) had the highest accuracy (85.87%) in predicting the outcomes of kidney stone endoscopic surgery.

THE INTRODUCTION OF KAZAKH LANGUAGE TO THE “SYMPTOM CHECKER” SYSTEM

This article studies the introduction of Kazakh language to the medical decision system called “Symptomchecker”. As a solution, model of the medical decision system was recreated from medical support system ISABEL and was made by the methodology method. For selecting and showing the final diagnosis the dataset was translated from Russian language to Kazakh using Google Translate and the website: sozdik.kz. The aim of this research work is to create the medical support system for people with knowledge of Kazakh language who don’t know other languages. The system was created in Android Studio for mobile devices. The user is displayed with a nitty gritty list of all conceivable symptoms seen in most of the commonly seen maladies overseen by selfcare.