Using inductive machine learning to support decision making in machining processes

2000 ◽  
Vol 43 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Bogdan Filipič ◽  
Mihael Junkar
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Machining Processes ◽  
Inductive Machine Learning ◽  
Support 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 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.

scholarly journals Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner’s hype cycle

2020 ◽  
Vol 5 (10) ◽  
pp. 593-603
Author(s):  
Jacobien H.F. Oosterhoff ◽  
Job N. Doornberg ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Orthopaedic Surgery ◽  
High Volume ◽  
Clinical Use ◽  
Hype Cycle ◽  
Computer Vision Applications ◽  
Near Future ◽  
Support Decision Making

Artificial Intelligence (AI) in general, and Machine Learning (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery. The greatest benefit of ML is in its ability to learn from real-world clinical use and experience, and thereby its capability to improve its own performance. Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients’ care and doctors’ workflows. The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment. Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks. In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. ‘If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine’.76 Cite this article: EFORT Open Rev 2020;5:593-603. DOI: 10.1302/2058-5241.5.190092

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