scholarly journals Technological Model using Machine Learning Tools to Support Decision Making in the Diagnosis and Treatment of Pediatric Leukemia

2021 ◽  
Author(s):  
Daniel Mendoza-Vasquez ◽  
Stephany Salazar-Chavez ◽  
Willy Ugarte
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Diagnosis And Treatment ◽  
Learning Tools ◽  
Pediatric Leukemia ◽  
Support Decision Making ◽  
Technological Model

scholarly journals Latest Tools for Data Mining and Machine Learning

2019 ◽  
Vol 8 (9S) ◽  
pp. 18-23 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Feature Selection ◽  
Open Source ◽  
Predictive Analysis ◽  
Learning Tools ◽  
Pros And Cons ◽  
Selection For ◽  
Extract Information

Nowadays, Data Mining is used everywhere for extracting information from the data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns which are used to extract information and knowledge for making decisions. Many open source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for Data Mining and predictive analysis. This paper discusses about different tools available for Data Mining and Machine Learning, followed by the description, pros and cons of these tools. The article provides details of all the algorithms like classification, regression, characterization, discretization, clustering, visualization and feature selection for Data Mining and Machine Learning tools. It will help people for efficient decision making and suggests which tool is suitable according to their requirement.

scholarly journals Bayesian Kernel Methods

2021 ◽  
Vol 6 (1) ◽  
pp. 26-39
Author(s):  
Arti Saxena ◽  
Vijay Kumar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Medical Decision Making ◽  
Kernel Method ◽  
Machine Learning Techniques ◽  
Medical Decision ◽  
Learning Tools ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Available Information

In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients, behaviour of the diseases, etc. are collected, and now it becomes the job of the practitioner at source to use the available data for diagnosing the diseases accurately and then prescribe the relevant treatment. Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. The inharmonious behavior of the data is the motivation behind the development of new tools and demonstrates the available information to some meaningful information for decision making. As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed, which suits the purpose of researchers in the enhancement of their research in the domain of medical decision making.

The Future of Emotions in the Workplace

2019 ◽  
pp. 193-215
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Dark Side ◽  
Use Of Technology ◽  
Improper Use ◽  
Emotions In The Workplace ◽  
Privacy Issues ◽  
Support Decision Making ◽  
State Of Art

In this chapter, the topic of artificial intelligence in the organisation will be presented. First of all, the authors start looking at the state of art in AI, one of the hot topics of the last decades. After discussing the practical uses of artificial intelligence in the organisation, they introduce the concept of emotional artificial intelligence that is linked to the ability of a machine to interpret human behaviour and adapt their responses accordingly. Artificial intelligence also offers interesting solutions for emotion analytics to support decision making, and to predict individuals' behaviour, whether in marketing or personnel management, among others. However, all this potential has an ethical dark side, linked to privacy issues, the loss of jobs to machines, or other threats to humanity caused by improper use of technology. While exploring more about machine learning, the authors reflect on some of the modern questions we face.

scholarly journals Supporting Real World Decision Making in Coronary Diseases Using Machine Learning

2021 ◽  
Vol 58 ◽  
pp. 004695802199733
Author(s):  
Peter Kokol ◽  
Jan Jurman ◽  
Tajda Bogovič ◽  
Tadej Završnik ◽  
Jernej Završnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Trees ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Learning Approaches ◽  
Support Decision Making

Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary artery diseases. While a plethora of studies reported high accuracy rates of machine learning algorithms (MLA) in medical applications, the majority of the studies used the cleansed medical data bases without the presence of the “real world noise.” Contrary, the aim of our study was to perform machine learning on the routinely collected Anonymous Cardiovascular Database (ACD), extracted directly from a hospital information system of the University Medical Centre Maribor). Many studies used tens of different machine learning approaches with substantially varying results regarding accuracy (ACU), hence they were not usable as a base to validate the results of our study. Thus, we decided, that our study will be performed in the 2 phases. During the first phase we trained the different MLAs on a comparable University of California Irvine UCI Heart Disease Dataset. The aim of this phase was first to define the “standard” ACU values and second to reduce the set of all MLAs to the most appropriate candidates to be used on the ACD, during the second phase. Seven MLAs were selected and the standard ACUs for the 2-class diagnosis were 0.85. Surprisingly, the same MLAs achieved the ACUs around 0.96 on the ACD. A general comparison of both databases revealed that different machine learning algorithms performance differ significantly. The accuracy on the ACD reached the highest levels using decision trees and neural networks while Liner regression and AdaBoost performed best in UCI database. This might indicate that decision trees based algorithms and neural networks are better in coping with real world not “noise free” clinical data and could successfully support decision making concerned with coronary diseasesmachine learning.

scholarly journals Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools

2019 ◽  
Vol 13 ◽  
Author(s):  
Grzegorz M. Wojcik ◽  
Jolanta Masiak ◽  
Andrzej Kawiak ◽  
Lukasz Kwasniewicz ◽  
Piotr Schneider ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Decision Making Process ◽  
Learning Tools ◽  
Quantitative Electroencephalography

scholarly journals Calibration drift in regression and machine learning models for acute kidney injury

2017 ◽  
Vol 24 (6) ◽  
pp. 1052-1061 ◽  
Author(s):  
Sharon E Davis ◽  
Thomas A Lasko ◽  
Guanhua Chen ◽  
Edward D Siew ◽  
Michael E Matheny
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Acute Kidney Injury ◽  
Regression Models ◽  
Kidney Injury ◽  
Learning Models ◽  
Personalized Risk ◽  
Support Decision Making ◽  
Over Time ◽  
Machine Learning Models

Abstract Objective Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population. Materials and Methods Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years. Results Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods. Conclusions Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

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