METHODS FOR FUZZY DEMAND ASSESSMENT FOR IT SPECIALTIES

The rapid development of information technologies and their penetration into various spheres of human activity cause a sharply increased demand for IT specialists, in many countries of the world far exceeding the supply on them. High rates of technological transformation contribute to the diversification of the IT segment of the labor market, on the one hand, stimulate the disappearance of some and the emergence of new IT specialties, on the other. This creates a discrepancy between the structure of IT-related education and the labor market demand for IT specialists of the required profile and determines the relevance of developing methods for assessing the demand for IT specialties. This article is devoted to the study and solution of the problem of identifying the demand for IT specialties in the absence of accurate and complete information about the situation in the IT market segment. For the assessment of IT specialties and their ranking by the degree of demand in the labor market, the tasks of making individual and group decisions in the context of fuzzy initial information are formulated and solved. The methodological basis of the tasks posed is multi-criteria decision support methods based on fuzzy relations of expert preferences. The proposed approach as a mathematical tool for minimizing the structural imbalance of supply and demand for IT specialties is one of the components of the system of intellectual management of the labor market of IT specialists. The latter is designed to support the adoption of scientifically based management decisions to eliminate the mismatch of supply and demand in the IT segment of the labor market in professional, quantitative and qualitative sections

Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform

This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problem

Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging

The article proposes the principles for the development of a fuzzy rule-based physician decision support system n to determine the stages of the most common hepatocellular carcinoma (HCC) among malignant tumors of liver. The stages of HCC, i.e., critical situations, are expressed by different combinations of clinical signs of input data and emerging clinical conditions. These combinations shape the multiplicity of possible situations (critical situations) by forming linguistic rules that are in fuzzy relations with one another. The article presents the task of developing a fuzzy rules-based system for HCC staging by classifying the set of possible situations into given classes. In order to solve the problem, fuzzy rules of clinical situations and critical situations deviated from them are developed according to the possible clinical signs of input data. The rules in accordance with the decision-making process are developed in two phases. In the first phase, three input data are developed: nine rules are developed to determine possible clinical conditions based on the number, size, and vascular invasion of tumor. In the second phase, seven rules are developed based on possible combinations of input data on the presence of lymph nodes and metastases in these nine clinical conditions. At this stage, the rules representing the fuzzification of results obtained are also described. The latter provide an interpretation of results and a decision on related stage of HCC. It also proposes a functional scheme of fuzzy rules-based system for HCC staging, and presents the working principle of structural blocks. The fuzzy rule-based system for HCC staging can be used to support physicians to make diagnostic and treatment decision

Lexicon-based sentiment analysis of medical data

The article explores the possibilities of applying sentiment analysis for the use of information collected in the medical social media environment in medical decision-making. Opinions and feedbacks of medical social media subjects (physician, patient, health institution, etc.) make media resources an important source of information. The information collected in these sources can be used to improve the quality of health care and make decisions, taking into account the public opinion. Researches in this field have actualized the application of artificial intelligence methods, i.e., sentiment analysis methods. In this regard, it segments the medical social media environment in accordance with user relationships, and shows the nature of the information collected on each segment and its importance in decision-making to improve the quality of medical services. The possibilities of applying the lexicon-based sentiment analysis method for studying and classifying the collected data are explained in detail. The open database cms_hospital_satisfaction_2019 by the Kaggle company is used, and the opinions collected from patients about the services provided by a specific medical center are analyzed. This study analyzes opinions using the Valence Aware Dictionary and Sentiment Reasoner lexicon and classifies them as neutral, positive and negative and the implementation of this process is described in stages. The importance of the obtained results in decision-making regarding the better organization, evaluation and improvement of the activity of the medical institution is show

Development of digital twin ecosystem and ontology in medicine

Summary: Providing citizens with high-quality and safe medical services, providing information support for medical research and continuous medical education, making both doctor’s decisions and management decisions necessitated the provision of tools to ensure complex digitization of healthcare. To achieve these goals, a wide range of modern technologies have emerged. One such technology is digital twin technology. Modern medicine, being formed in the environment of Health 4.0, includes not only the treatment of patients, but also the management of healthcare, the prevention of diseases and the processes of health restoration. With the increasing popularity of information communication technologies, people’s demand for health services is shifting from offline service to new online models. Currently, the field of online medicine is not developed enough to serve the elderly, chronically ill people and the people with infectious diseases. Using the advantages of digital twins in solving these problems can give positive results. The article describes the nature, capabilities and applications of digital twin technology. The principles of the formation of the medical digital twin ecosystem are developed to ensure citizens’ accessibility to medical services and to make both medical and managerial decisions. The architecture and structural components of the digital twin ecosystem providing the connection between physical medical objects (patient, hospital, doctor, etc.) and their virtual images are shown. An ontological model for the staged construction and functionalization of the general DT of healthcare is proposed and its hierarchical architecture is establishe

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1

Розробка методики прийняття рішень на основі сентимент-аналізу даних краудсорсингу в медичних соціальних ресурсах

The object of the study is the decision-making modeling in the context of medical social media to increase the clinics’ effectiveness. The problem is to classify the patient reviews collected in the patient-clinic segment of the medical social media and to identify the situation related to the clinics’ activity by revealing the criteria characterizing the clinics’ activity out of the opinions. The proposed technique refers to lexicon-based sentiment analysis of opinions, the classification based on Valence Aware Dictionary and Sentiment Reasoner (VADER), the verification of the results accuracy with Multinomial Naive Bayes and Support Vector Machine, the manual sentiment analysis of opinions to detect criteria and the classification of opinions according to each criterion. Using this technique, out of 442587 patient reviews obtained from database cms_hospital_satisfaction_2020 of the Kaggle company generated on the basis of crowdsourcing of patient reviews on medical social media, 218914 patient reviews are classified as positive, 190360 – as neutral, and 33313 – as negative. The results accuracy is verified, and the clinics are rated by the «positive» opinions. 6 new criteria characterizing the clinics’ activity are discovered, and the identification of the situation related to the clinics’ activity based on the comparison of «positive» and «negative» opinions according to each criterion is presented. The possibility of using the results obtained from the identification to increase the clinics’ efficiency in making decisions is shown. The results obtained in this study can be used to improve the clinics’ performance according to public opinion. This opportunity involves the crowdsourcing of opinions about the clinic in the medical social media environment and the collection of opinions in a structured way.Об’єктом дослідження є моделювання прийняття рішень у контексті медичних соціальних мереж для підвищення ефективності клінік. Проблема полягає в тому, щоб класифікувати відгуки пацієнтів, зібрані в сегменті пацієнт-клініка медичних соціальних медіа, і виявити ситуацію, пов’язану з діяльністю клінік, виявивши критерії, що характеризують діяльність клінік поза відгуками. Запропонована методика стосується аналізу настроїв думок на основі лексикону, класифікації на основі словника Valence Aware та Sentiment Reasoner (VADER), перевірки точності результатів за допомогою Multinomial Naive Bayes and Support Vector Machine, ручного аналізу настроїв думок для виявлення критерії та класифікація думок відповідно до кожного критерію. За допомогою цієї методики з 442587 відгуків пацієнтів, отриманих з бази даних cms_hospital_satisfaction_2020 компанії Kaggle, згенерованої на основі краудсорсингу відгуків пацієнтів у медичних соціальних мережах, 218914 відгуків пацієнтів класифікуються як позитивні, 190360 – як нейтральні, а 33313 – як негативні. Достовірність результатів перевіряється, а клініки оцінюються «позитивними» відгуками. Виявлено 6 нових критеріїв, що характеризують діяльність клінік, і наведено ідентифікацію ситуації, пов’язаної з діяльністю клінік, на основі порівняння «позитивних» і «негативних» думок за кожним критерієм. Показано можливість використання результатів ідентифікації для підвищення ефективності прийняття рішень клініками. Результати, отримані в цьому дослідженні, можуть бути використані для покращення роботи клінік відповідно до громадської думки. Ця можливість передбачає краудсорсинг думок про клініку в середовищі медичних соціальних мереж і збір думок у структурований спосіб

Synthesis of Decision Making in A Distributed Intelligent Personnel Health Management System on Offshore Oil Platform

This paper proposes a methodological approach for the decision synthesis in a geographically distributed intelligent health management system for oil workers working in offshore industry. The decision-making methodology is based on the concept of a person-centered approach to managing the health and safety of personnel, which implies the inclusion of employees as the main component in the control loop. This paper develops a functional model of the health management system for workers employed on offshore oil platforms and implements it through three phased operations that is monitoring and assessing the health indicators and environmental parameters of each employee, and making decisions. These interacting operations combine the levels of a distributed intelligent health management system. The paper offers the general principles of functioning of a distributed intelligent system for managing the health of workers in the context of structural components and computing platforms. It presents appropriate approaches to the implementation of decision support processes and describes one of the possible methods for evaluating the generated data and making decisions using fuzzy pattern recognition. The models of a fuzzy ideal image and fuzzy real images of the health status of an employee are developed and an algorithm is described for assessing the deviation of generated medical parameters from the norm. The paper also compiles the rules to form the knowledge bases of a distributed intelligent system for remote continuous monitoring. It is assumed that embedding this base into the intelligent system architecture will objectively assess the trends in the health status of workers and make informed decisions to eliminate certain problem