Agglomerative Hierarchical Algorithm Research Articles

Analysis of the effectiveness of clustering algorithms for multimodal samples using computer simulation of an educational experiment

Introduction. The article is devoted to the problem of primary data processing of pedagogical experiments having a multimodal character. The purpose of the study is to identify the most effective and universal clustering algorithms for pedagogical experiments. Materials and Methods. The study used the method of modeling a pedagogical experiment. The analysis of 5 clustering algorithms is conducted. The effectiveness of clustering algorithms was evaluated based on the proportion of observations with clustering errors at various tolerance levels and the Jacquard similarity coefficient. Regression analysis was used to assess the influence of modeling parameters of a pedagogical experiment and indicators of descriptive statistics on the effectiveness of clustering algorithms. Results. The assessment of the effectiveness of various data clustering algorithms is provided, as well as a correlation and regression analysis of factors affecting clustering efficiency indicators was carried out. Conclusions. The most effective clustering algorithms for multimodal samples include the K-means algorithm and the agglomerative hierarchical algorithm. The results obtained in this research can be used for statistical analysis of pedagogical, psychological, sociological, biological and medical research data.

Uncovering emotion sequence patterns in different interaction groups using deep learning and sequential pattern mining

AbstractBackgroundInvestigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy.ObjectivesThis research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums.MethodsData from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions.Results and ConclusionsUsing an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed.

Correction of the Values of the Classification Feature of Objects on the Example of the Diagnosis of Multiple Myeloma

The clinical features of changes in multiple myeloma indicators of different types associated with the gender of patients (objects) are considered. The methods of data mining examine the truth of the statement about the presence of many patients for whom gender is not significant in making a diagnosis. It is proposed to use the preprocessing of heterogeneous data to unify the description of objects in the binary space. The conditions for selecting and removing noise features from the set are determined. In order to reduce the dimensionality of the space, latent features are calculated by groups of binary generalized estimates of objects. A criterion is proposed for dividing patients into the optimal number of groups, taking into account their gender authenticity. From these groups, a new classification of objects is formed, differentiated by gender. The formation process is illustrated through the visualization of object descriptions, recognition accuracy and selection of informative feature sets according to the new classification. The selection procedure is implemented according to the rules of a hierarchical agglomerative algorithm. The property of invariance to the measurement scales of quantitative traits is an important argument for using the obtained results on data samples from the general population.

РЕГІОНАЛЬНА ДИФЕРЕНЦІАЦІЯ СПОЖИВЧИХ ЦІН В УКРАЇНІ

Consumer prices are a litmus test for structural changes in any society. Rapid price changes are evidence of serious problems and are the result of the influence of many factors. It could be easily seen from the events in Ukraine in 2022. The occupation and then partial de-occupation of large areas of the country, and the difficulties of ensuring life in the front-line areas led to a rapid rise in prices and inflation in the entire country. The article examines the impact of a large-scale war on consumer prices by Ukraine regions in January 2022 and January 2023. For this, the methodology of collecting information on consumer prices in the territory of Ukraine and the peculiarities of determining the average values, on which the calculations in this work are based, were studied. Also, were analyzed the features of hierarchical clustering algorithms and the appropriateness of dendrograms usage. During the research, the clustering (regional differentiation) of regions by consumer prices was carried out using a hierarchical agglomerative algorithm based on the Ward method with Euclidean distance, using normalized (linearly transformed) data (prices in the specified periods). The research also includes the comparison of the formed groups with the existing division of Ukraine into economic regions. In addition, the analysis of the obtained results of the differentiation of regions, as well as the factors that influenced the sharp growth of consumer prices, and above all food products, was carried out. From the results, it can be easily seen that districts in which the country was divided by consumer prices are weakly correlated with nine economic districts in which the country is divided officially. Consumer prices are affected by factors other than those considered during the division into official districts. The uneven impact of the war on different regions led to a radical change in the division, as the growth of prices in the front-line zones exceeded even those in the capital. Especially, it refers to the Kherson region, more precisely, that part of it that was freed from the long-term occupation. In this region were recorded the highest prices in comparison with all the other regions.

COMPARATIVE ANALYSIS OF CLUSTER VALIDITY INDICES IN TERMS OF CONSISTENCY

Cluster analysis is one of the key issues in Data Mining, the most important stage of the Knowledge Discovery from Data (KDD) process, and is widely used. There are 3 main tasks of cluster analysis: determining the optimal number of clusters, clustering algorithms and evaluating the quality of clustering. One of the most important steps in cluster analysis is to evaluate the quality of clustering. A number of indices have been proposed to assess the outcome of clustering. The analysis shows that these indices, which are used to assess the quality of clustering, often show inconsistent results. Therefore, extensive research has recently been conducted on the study of indices and new indices are proposed. The article examines a number of internal and external evaluation indices. Different size data sets are taken and k-means, k-medoids, agglomerative hierarchical, BIRCH and OPTICS algorithms are applied to them. A number of internal and external evaluation indices are used to assess the results of the experiment, and the results are analyzed comparatively. Experiments show that Ac, Pr, Rc and F-m indices show similar results in in group determining in a given clustering structure.

Entia Non Sunt Multiplicanda … Shall I look for clusters in my cognitive data?

Unsupervised clustering methods are increasingly being applied in psychology. Researchers may use such methods on multivariate data to reveal previously undetected sub-populations of individuals within a larger population. Realistic research scenarios in the cognitive science may not be ideally suited for a successful use of these methods, however, as they are characterized by modest effect sizes, limited sample sizes, and non-orthogonal indicators. This combination of characteristics even presents a high risk of detecting non-existing clusters. A systematic review showed that, among 191 studies published in 2016-2020 that used different clustering methods to classify human participants, the median sample size was only 322, and a median of 3 latent classes/clusters were detected. None of them concluded in favor of a one-cluster solution, potentially giving rise to an extreme publication bias. Dimensionality reduction techniques are almost never used before clustering. In a subsequent simulation study, we examined the performance of popular clustering techniques, including Gaussian mixture model, a partitioning, and a hierarchical agglomerative algorithm. We focused on their ability to detect the correct number of clusters, and on their classification accuracy. Under a reasoned set of scenarios that we considered plausible for the cognitive research, none of the methods adequately discriminates between one vs two true clusters. In addition, non-orthogonal indicators lead to a high risk of incorrectly detecting multiple clusters where none existed, even in the presence of only modest correlation (a frequent case in psychology). In conclusion, it is hard for researchers to be in a condition to achieve a valid unsupervised clustering for inferential purposes with a view to classifying individuals.

Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns.

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.

Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases.

Simple SummarySolvent-Accessible Surface Area (SASA) as the one dimensional structure property of the protein considers as the measuring the exposure of an amino acid residue to the solvent in one protein. It is an important structural property as the active sites of proteins are mostly located on the protein surfaces. The aim of this paper is to provide the clear information on different Amycolatopsis eburnea lipases based on the SASA patterns. This information could help in recognizing the structural stability and conformation as well as precise clustering them for revealing lipase evolution.The wealth of biological databases provides a valuable asset to understand evolution at a molecular level. This research presents the machine learning approach, an unsupervised agglomerative hierarchical clustering analysis of invariant solvent accessible surface areas and conserved structural features of Amycolatopsis eburnea lipases to exploit the enzyme stability and evolution. Amycolatopsis eburnea lipase sequences were retrieved from biological database. Six structural conserved regions and their residues were identified. Total Solvent Accessible Surface Area (SASA) and structural conserved-SASA with unsupervised agglomerative hierarchical algorithm were clustered lipases in three distinct groups (99/96%). The minimum SASA of nucleus residues was related to Lipase-4. It is clearly shown that the overall side chain of SASA was higher than the backbone in all enzymes. The SASA pattern of conserved regions clearly showed the evolutionary conservation areas that stabilized Amycolatopsis eburnea lipase structures. This research can bring new insight in protein design based on structurally conserved SASA in lipases with the help of a machine learning approach.

A Comparative Effectiveness of Hierarchical and Non-hierarchical Regionalisation Algorithms in Regionalising the Homogeneous Rainfall Regions

Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms requiring post-processing techniques to validate and interpret the analysis results. The main objective of this study is to investigate the effectiveness of the automated agglomerative hierarchical and non-hierarchical regionalisation algorithms in identifying the homogeneous rainfall regions based on a new statistically significant difference regionalised feature set. To pursue this objective, this study collected 20 historical monthly rainfall time-series data from the rain gauge stations located in the Kuantan district. In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan- Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Furthermore, FKNH, HKNH, and LKNH yielded the highest regionalisation accuracy compared to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Based on the regionalisation results yielded in this study, the reliability and accuracy that assessed the risk of extreme hydro-meteorological events for the Kuantan district can be improved. In particular, the regional quantile estimates can provide a more accurate estimation compared to at-site quantile estimates using an appropriate statistical distribution.

CLUSTER ANALYSIS OF THE EUROPEAN UNION GENDER EQUALITY AND ECONOMIC DEVELOPMENT

Purpose – The article aims at discovering classes and clusters of EU member-states considering their levels of economic development and gender equality to foster the enhancement of EU cohesion policy. Research methodology – The methodology includes the grouping by two parameters, economic development and gender equality, and the cluster analysis, the “far neighbor principle”, agglomerative hierarchical classification algorithm and the usual Euclidean distance as the distance between objects. Findings – There are no gender equality laggards among EU member-states. More developed countries belong to gender equality leaders, while there are two gender equality leaders and one gender equality adopter among transition countries. The group of less developed countries consists of six gender equality leaders and seven gender equality adopters. Research limitations – The results of cluster analysis may be impacted by off-shore activity of Ireland and Luxembourg. Practical implications – The EU supranational bodies can use our results to develop more efficient cohesion policy tools to ensure the adherence to the principle of gender equality. Originality/Value – The study is a pioneer one in determining nine classes and five clusters of EU member-states considering their levels of economic development and gender equality, as well as in introducing three types of countries depending on their level of gender equality, namely gender equality leaders, adopters, and laggards.

Positively Correlated Samples Save Pooled Testing Costs.

The group testing approach, which achieves significant cost reduction over the individual testing approach, has received a lot of interest lately for massive testing of COVID-19. Many studies simply assume samples mixed in a group are independent. However, this assumption may not be reasonable for a contagious disease like COVID-19. Specifically, people within a family tend to infect each other and thus are likely to be positively correlated. By exploiting positive correlation, we make the following two main contributions. One is to provide a rigorous proof that further cost reduction can be achieved by using the Dorfman two-stage method when samples within a group are positively correlated. The other is to propose a hierarchical agglomerative algorithm for pooled testing with a social graph, where an edge in the social graph connects frequent social contacts between two persons. Such an algorithm leads to notable cost reduction (roughly 20–35%) compared to random pooling when the Dorfman two-stage algorithm is applied.

Forming clusters based on strategic partnerships and circular economy for biogas production: A GIS analysis for optimal location

The aim of this paper is twofold: (i) to build a model by combining an agglomerative hierarchical algorithm with multicriteria analysis and geographic information systems (GIS) in order to identify whether there are opportunities to create strategic partnerships based on geographic coordinates of raw material suppliers and the volume of raw material supply; and (ii) to apply the model in a case study in order to identify suitable locations for building a biodigester, thus establishing strategic partnerships and circular economy-based practices. The model involves four stages: (1) defining the study site, (2) building the set of cluster solutions, (3) defining the suitable locations for building a biodigester, and (4) conducting analyses of feasibility of strategic partnership and sensitivity. The model was applied in a small region and it was analyzed the possibility of creating two or three clusters. The use of the model allowed, from both quantitative and qualitative perspectives, to preliminarily identify the possibility of creating a strategic partnership, and also determine the location of a biodigester. The case study applied in southern Brazil took place in a region with high potential for generating biogas from agro-waste. A geographic map with roads, rivers, and constraints was built. Strategic partnerships, sensitivity analysis, and circular economy-based practices were discussed. The results of this work could provide useful information when using the model for other regions, and the model could easily incorporate further criteria and conditions, making it flexible and tailored to users' needs.

Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection

Despite being a central concept in cheminformatics, molecular similarity has so far been limited to the simultaneous comparison of only two molecules at a time and using one index, generally the Tanimoto coefficent. In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over “traditional” pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the n-ary and binary indices rank the data in the same way. We demonstrate the use of the new n-ary similarity metrics on t-distributed stochastic neighbor embedding (t-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at: https://github.com/ramirandaq/MultipleComparisons

Structural textile pattern recognition and processing based on hypergraphs

The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing. To facilitate the clustering and search, we introduce an approach for recognising similar weaving patterns based on their structures for textile archives. We first represent textile structures using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs. Then, the resulting multisets are clustered using various distance measures and various clustering algorithms (K-Means for simplicity and hierarchical agglomerative algorithms for precision). We evaluate the different variants of our approach experimentally, showing that this can be implemented efficiently (meaning it has linear complexity), and demonstrate its quality to query and cluster datasets containing large textile samples. As, to the best of our knowledge, this is the first practical approach for explicitly modelling complex and irregular weaving patterns usable for retrieval, we aim at establishing a solid baseline.

Comparison of Hierarchical Agglomerative Algorithms for Clustering Medical Documents

Comparison of Hierarchical Agglomerative Algorithms for Clustering Medical Documents

Cancel-for-Any-Reason Insurance Recommendation Using Customer Transaction-Based Clustering

In the travel insurance industry, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cancel-for-any-reason</i> insurance, also known as a cancellation protection service (CPS), is a recent attempt to strike a balance between customer satisfaction and service provider (SP) profits. However, some exceptional circumstances, particularly the COVID-19 pandemic, have led to a dramatic decrease in SP revenues, especially for non-refundable tickets purchased early with CPS. This paper begins by presenting a risk group segmentation of customers in an online ticket reservation system. Then, a CPS fee is recommended depending on the different customer risk groups provided by the cluster segmentation via different clustering algorithms such as centroid-based K-means, hierarchical agglomerative, DBSCAN, and artificial neural network-based SOM algorithms. According to the implemented cluster metrics, which include the Silhouette index, Davies-Bouldin index, Entropy index, and DBCV index, the SOM algorithm presents the most appropriate result. After predicting the new customer cluster, a CPS fee will be calculated with the proposed adaptive CPS method based on the cluster segmentation weights. Determining the weight of each cluster is related to the total CPS revenue threshold for all clusters defined by the SP. Therefore, to avoid a loss for SPs, the total CPS revenue will be kept constant with the threshold that the SP has been adjusted. The experimental results based on real-world data show that the risk group segmentation of customers helps to maintain a balance between CPS fees and SP profits. Finally, according to the calculated weights, the proposed model pegs the SP gain/loss variation with a 0.00012 exchange ratio.

Ship Target Imaging in Airborne SAR System Based on Automatic Image Segmentation and ISAR Technique

The airborne synthetic aperture radar (SAR) image of ship target will be blurred for the complex motion of the target, which will influence the performance of feature extraction and target classification. In this article, a refocusing method of ship target in airborne SAR image though the inverse synthetic aperture radar (ISAR) technique is presented. The novel contributions of this article can be summarized as follows: 1) the residual phase of processed signal is analyzed in detail to illustrate the necessity of adopting ISAR technique; 2) a valid, labor-saving, and automatic image segmentation approach via clustering algorithm is put forward for automatic data extraction and inverse mapping. The clustering algorithm we choose is the agglomerative hierarchical cluster algorithm, which does not require the number of categories in advance; 3) the refocused individual ISAR image of individual ship target is yielded though the range-instantaneous Doppler algorithm. Finally, the simulated and real measured data are processed, and imaging results verify the effectiveness of the novel algorithm presented in this article.

Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting

In recent years, many buildings have been fitted with smart meters, from which high-frequency energy data is available. However, extracting useful information efficiently has been imposed as a problem in utilizing these data. In this study, we analyzed district heating smart meter data from 61 buildings in Copenhagen, Denmark, focused on the peak load quantification in a building cluster and a case study on load shifting. The energy consumption data were clustered into three subsets concerning seasonal variation (winter, transition season, and summer), using the agglomerative hierarchical algorithm. The representative load profile obtained from clustering analysis were categorized by their profile features on the peak. The investigation of peak load shifting potentials was then conducted by quantifying peak load concerning their load profile types, which were indicated by the absolute peak power, the peak duration, and the sharpness of the peak. A numerical model was developed for a representative building, to determine peak shaving potentials. The model was calibrated and validated using the time-series measurements of two heating seasons. The heating load profiles of the buildings were classified into five types. The buildings with the hat shape peak type were in the majority during the winter and had the highest load shifting potential in the winter and transition season. The hat shape type’s peak load accounted for 10.7% of the total heating loads in winter, and the morning peak type accounted for 12.6% of total heating loads in the transition season. The case study simulation showed that the morning peak load was reduced by about 70%, by modulating the supply water temperature setpoints based on weather compensation curves. The methods and procedures used in this study can be applied in other cases, for the data analysis of a large number of buildings and the investigation of peak loads.

Community detection‐based deep neural network architectures: A fully automated framework based on Likert‐scale data

Deep neural networks (DNNs) have emerged as a state‐of‐the‐art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio‐demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures.

Clinical pathways analysis of patients in medical institutions based on hard and fuzzy clustering methods

Modeling the processes in a healthcare system plays a large role in understanding its activities and serves as the basis for increasing the efficiency of medical institutions. The tasks of analyzing and modeling large amounts of urban healthcare data using machine learning methods are of particular importance and relevance for the development of industry solutions in the framework of digitalization of the economy, where data is the key factor in production. The problem of automatic analysis and determination of clinical pathways groups of patients based on clustering methods is considered in this research. Existing projects in this area reflect a great interest on the part of the scientific community in such studies; however, there is a need to develop a number of methodological approaches for their further practical application in urban outpatient institutions, taking into account the specifics of the organization being analyzed. The aim of the study is to improve the quality of management and segmentation of patient input flow in urban medical institutions based on cluster analysis methods for the further development of recommendation services. One approach to achieving this goal is the development and implementation of clinical pathways, or patient trajectories. In general, the clinical pathway of a patient might be interpreted as the trajectory when receiving medical services in respective institutions. The approach of developing groups of patient routes by the hierarchical agglomerative algorithm with the Ward method and Additive Regularization of Topic Models (ARTM) is presented in this article. A computational experiment based on public data on the routes of patients with a diagnosis of sepsis is described. One feature of the proposed approach is not just the automation of the determination of similar groups of patient trajectories, but also the consideration of clinical pathways patterns to form recommendations for organizing the resource allocation of a medical institution. The proposed approach to segmenting the input heterogeneous flow of patients in urban medical institutions on the basis of clustering consists of the following steps: 1) preparing the data of the medical institution in the format of an event log; 2) encoding patient routes; 3) determination of the upper limit of the clinical pathway length; 4) hierarchical agglomerative clustering; 5) additive regularization of topic models (ARTM); 6) identifying popular patient route patterns. The resulting clusters of routes serve as the foundation for the further development of a simulation model of a medical institution and provide recommendations to patients. In addition, these groups may underlie the development of the robotic process automation system (RPA), which simulates human actions and allows you to automate the interpretation of data to manage the resources of the institution.