scholarly journals Laboratory Demand Management Strategies—An Overview

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1141
Author(s):  
Cornelia Mrazek ◽  
Elisabeth Haschke-Becher ◽  
Thomas K. Felder ◽  
Martin H. Keppel ◽  
Hannes Oberkofler ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Laboratory Test ◽  
Demand Management ◽  
Management Strategies ◽  
Test Selection ◽  
Automated Algorithms

Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools.

scholarly journals Modeling Pinot Noir Aroma Profiles Based on Weather and Water Management Information Using Machine Learning Algorithms: A Vertical Vintage Analysis Using Artificial Intelligence

Foods ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 33 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Eden Tongson ◽  
Damir D. Torrico ◽  
Claudia Gonzalez Viejo
Keyword(s):  
Artificial Intelligence ◽  
Water Management ◽  
Machine Learning ◽  
Management Strategies ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Pinot Noir ◽  
Wine Aroma ◽  
Management Information ◽  
Artificial Neural Network Ann

Wine aroma profiles are determinant for the specific style and quality characteristics of final wines. These are dependent on the seasonality, mainly weather conditions, such as solar exposure and temperatures and water management strategies from veraison to harvest. This paper presents machine learning modeling strategies using weather and water management information from a Pinot noir vineyard from 2008 to 2016 vintages as inputs and aroma profiles from wines from the same vintages assessed using gas chromatography and chemometric analyses of wines as targets. The results showed that artificial neural network (ANN) models rendered the high accuracy in the prediction of aroma profiles (Model 1; R = 0.99) and chemometric wine parameters (Model 2; R = 0.94) with no indication of overfitting. These models could offer powerful tools to winemakers to assess the aroma profiles of wines before winemaking, which could help adjust some techniques to maintain/increase the quality of wines or wine styles that are characteristic of specific vineyards or regions. These models can be modified for different cultivars and regions by including more data from vertical vintages to implement artificial intelligence in winemaking.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Computing Possible Futures

2019 ◽  
Author(s):  
William B. Rouse
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Modeling ◽  
Mental Model ◽  
Data Analytics ◽  
Computational Models ◽  
Senior Managers ◽  
Interactive Visualizations ◽  
Use Of Models ◽  
Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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