scholarly journals The Model of Human Capital Management in Decision Support Systems

2019 ◽  
Vol 81 (3) ◽  
pp. 290-298
Author(s):  
O. D. Kazakov ◽  
N. Y. Azarenko
Keyword(s):  
Machine Learning ◽  
Human Capital ◽  
Decision Support ◽  
Random Forest ◽  
Digital Economy ◽  
Machine Learning Algorithms ◽  
System Model ◽  
Capital Management ◽  
Human Capital Management ◽  
Economic Tools

A system model of human capital management has been developed, which, according to the authors, can be an important addition to building an effective decision support system in the field of socio-economic development of the region. The interaction of the subjects of the model can occur in order to implement innovative processes in IT organizations. The developed system model of human capital management takes into account the peculiarities of human capital management in the digital economy and includes three subsystems: “The structure of human capital”, “Tools for the development of human capital”, “Organizational and economic tools for managing human capital”. The work used data obtained in the analysis of more than 100 innovative enterprises engaged in research, including in the field of information technology. As a result of the study, marked-up data were generated on the level of development efficiency of human capital based on the initial processing of financial statements of innovative organizations. To determine the directions of improving the quality of human capital, an approach to assessing the level of efficiency of its development based on the machine learning model is presented. The values ??of the quality metrics of the following machine learning algorithms for solving the problem are presented: linear regression; Random Forest; nearest neighbors method. To classify the region’s enterprises according to the level of development efficiency of human capital based on open financial information, the Random Forest algorithm was chosen. 11 most accurate classification rules in a hierarchical sequential structure were identified and formulated. This will allow for more complete consideration of all aspects of intellectual resources management in the digital economy.

Avatars-Based Decision Support System Using Blockchain and Knowledge Sharing for Processes Simulation

2021 ◽  
Vol 17 (1) ◽  
pp. 72-92
Author(s):  
Vardan Mkrttchian
Keyword(s):  
Human Capital ◽  
Decision Support ◽  
Knowledge Sharing ◽  
Process Simulation ◽  
Efficient Algorithm ◽  
Capital Management ◽  
Human Capital Management ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article is an enhancement of the chapter “About Digital Avatars for Control in Virtual Industries” in the book Big Data and Knowledge Sharing in Virtual Organizations. The article discusses the capabilities of the R language for modeling Levy processes that currently most closely correspond to the nature of the organizational learning movements in sliding mode. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed at R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed at R language. Overview of CGMY process simulation in practice is use for human capital management in the context of the implementation of digital intelligent decision support systems and knowledge management and for digital intelligent design of avatar-based control with application to human capital management.

scholarly journals Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

2021 ◽  
Vol 17 (11) ◽  
pp. 60
Author(s):  
Halima EL Hamdaoui ◽  
Said Boujraf ◽  
Nour El Houda Chaoui ◽  
Badr Alami ◽  
Mustapha Maaroufi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Support ◽  
Random Forest ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Random Forest Algorithm ◽  
Adaboost Algorithm

heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

scholarly journals Early Detection of Septic Shock Onset Using Interpretable Machine Learners

2021 ◽  
Vol 10 (2) ◽  
pp. 301
Author(s):  
Debdipto Misra ◽  
Venkatesh Avula ◽  
Donna M. Wolk ◽  
Hosam A. Farag ◽  
Jiang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Septic Shock ◽  
Logistic Regression ◽  
Decision Support ◽  
Random Forest ◽  
Decision Support System ◽  
Support System ◽  
Machine Learning Algorithms ◽  
Sepsis Definition ◽  
Time Of Admission

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare & Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Retrospective Study ◽  
Random Forest ◽  
Precocious Puberty ◽  
Prediction Models ◽  
Central Precocious Puberty ◽  
Machine Learning Algorithms ◽  
Stimulation Test ◽  
Gnrh Analogue ◽  
Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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