scholarly journals Quasi-Deterministic Processes with Monotonic Trajectories and Unsupervised Machine Learning

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2301
Author(s):  
Andrey V. Orekhov
Keyword(s):  
Machine Learning ◽  
Solid Mechanics ◽  
Approximation Error ◽  
Strain Curve ◽  
Qualitative Change ◽  
Integral Estimation ◽  
Number Sequences ◽  
Deterministic Processes ◽  
Polymerase Chain ◽  
Proportionality Limit

This paper aims to consider approximation-estimation tests for decision-making by machine-learning methods, and integral-estimation tests are defined, which is a generalization for the continuous case. Approximation-estimation tests are measurable sampling functions (statistics) that estimate the approximation error of monotonically increasing number sequences in different classes of functions. These tests make it possible to determine the Markov moments of a qualitative change in the increase in such sequences, from linear to nonlinear type. If these sequences are trajectories of discrete quasi-deterministic random processes, then moments of change in the nature of their growth and qualitative change in the process match up. For example, in cluster analysis, approximation-estimation tests are a formal generalization of the “elbow method” heuristic. In solid mechanics, they can be used to determine the proportionality limit for the stress strain curve (boundaries of application of Hooke’s law). In molecular biology methods, approximation-estimation tests make it possible to determine the beginning of the exponential phase and the transition to the plateau phase for the curves of fluorescence accumulation of the real-time polymerase chain reaction, etc.

scholarly journals Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests

2020 ◽  
Author(s):  
Thomas Tschoellitsch ◽  
Martin Dünser ◽  
Carl Böck ◽  
Karin Schwarzbauer ◽  
Jens Meier
Keyword(s):  
Machine Learning ◽  
Polymerase Chain Reaction ◽  
Characteristic Curve ◽  
Cohort Analysis ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Blood Tests ◽  
Routine Blood ◽  
Machine Learning Model ◽  
Polymerase Chain

Abstract Objective The diagnosis of COVID-19 is based on the detection of SARS-CoV-2 in respiratory secretions, blood, or stool. Currently, reverse transcription polymerase chain reaction (RT-PCR) is the most commonly used method to test for SARS-CoV-2. Methods In this retrospective cohort analysis, we evaluated whether machine learning could exclude SARS-CoV-2 infection using routinely available laboratory values. A Random Forests algorithm with 1353 unique features was trained to predict the RT-PCR results. Results Out of 12,848 patients undergoing SARS-CoV-2 testing, routine blood tests were simultaneously performed in 1528 patients. The machine learning model could predict SARS-CoV-2 test results with an accuracy of 86% and an area under the receiver operating characteristic curve of 0.90. Conclusion Machine learning methods can reliably predict a negative SARS-CoV-2 RT-PCR test result using standard blood tests.

scholarly journals Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach

2021 ◽  
Vol 10 (4) ◽  
pp. 570
Author(s):  
María A Callejon-Leblic ◽  
Ramon Moreno-Luna ◽  
Alfonso Del Cuvillo ◽  
Isabel M Reyes-Tejero ◽  
Miguel A Garcia-Villaran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Modelling Framework ◽  
Visual Analog Scales ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
Taste Disorders ◽  
Polymerase Chain ◽  
Fold Cross Validation ◽  
Validation Scheme ◽  
Control Study

The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.

scholarly journals Topological Optimization of Artificial Neural Networks to Estimate Mechanical Properties in Metal Forming Using Machine Learning

Metals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1289
Author(s):  
David Merayo ◽  
Alvaro Rodríguez-Prieto ◽  
Ana María Camacho
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Aluminum Alloys ◽  
Metal Forming ◽  
Strain Curve ◽  
Topological Optimization ◽  
Plastic Behavior ◽  
Input Dataset ◽  
Wrought Aluminum Alloys ◽  
Fully Connected

The ability of a metal to be subjected to forming processes depends mainly on its plastic behavior and, thus, the mechanical properties belonging to this region of the stress–strain curve. Forming techniques are among the most widespread metalworking procedures in manufacturing, and aluminum alloys are of great interest in fields as diverse as the aerospace sector or the food industry. A precise characterization of the mechanical properties is crucial to estimate the forming capability of equipment, but also for a robust numerical modeling of metal forming processes. Characterizing a material is a very relevant task in which large amounts of resources are invested, and this paper studies how to optimize a multilayer neural network to be able to make, through machine learning, precise and accurate predictions about the mechanical properties of wrought aluminum alloys. This study focuses on the determination of the ultimate tensile strength, closely related to the strain hardening of a material; more precisely, a methodology is developed that, by randomly partitioning the input dataset, performs training and prediction cycles that allow estimating the average performance of each fully-connected topology. In this way, trends are found in the behavior of the networks, and it is established that, for networks with at least 150 perceptrons in their hidden layers, the average predictive error stabilizes below 4%. Beyond this point, no really significant improvements are found, although there is an increase in computational requirements.

scholarly journals Accuracy of Conventional and Machine Learning Enhanced Chest Radiography for the Assessment of COVID-19 Pneumonia: Intra-Individual Comparison with CT

2020 ◽  
Vol 9 (11) ◽  
pp. 3576
Author(s):  
Katharina Martini ◽  
Christian Blüthgen ◽  
Joan E. Walter ◽  
Michael Messerli ◽  
Thi Dan Linh Nguyen-Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Interpretation ◽  
Conventional Radiography ◽  
Control Group ◽  
Disease Extent ◽  
Confidence Levels ◽  
Individual Comparison ◽  
Polymerase Chain ◽  
The Cost ◽  
Detection And Quantification

Purpose: To evaluate diagnostic accuracy of conventional radiography (CXR) and machine learning enhanced CXR (mlCXR) for the detection and quantification of disease-extent in COVID-19 patients compared to chest-CT. Methods: Real-time polymerase chain reaction (rt-PCR)-confirmed COVID-19-patients undergoing CXR from March to April 2020 together with COVID-19 negative patients as control group were retrospectively included. Two independent readers assessed CXR and mlCXR images for presence, disease extent and type (consolidation vs. ground-glass opacities (GGOs) of COVID-19-pneumonia. Further, readers had to assign confidence levels to their diagnosis. CT obtained ≤ 36 h from acquisition of CXR served as standard of reference. Inter-reader agreement, sensitivity for detection and disease extent of COVID-19-pneumonia compared to CT was calculated. McNemar test was used to test for significant differences. Results: Sixty patients (21 females; median age 61 years, range 38–81 years) were included. Inter-reader agreement improved from good to excellent when mlCXR instead of CXR was used (k = 0.831 vs. k = 0.742). Sensitivity for pneumonia detection improved from 79.5% to 92.3%, however, on the cost of specificity 100% vs. 71.4% (p = 0.031). Overall, sensitivity for the detection of consolidation was higher than for GGO (37.5% vs. 70.4%; respectively). No differences could be found in disease extent estimation between mlCXR and CXR, even though the detection of GGO could be improved. Diagnostic confidence was better on mlCXR compared to CXR (p = 0.013). Conclusion: In line with the current literature, the sensitivity for detection and quantification of COVID-19-pneumonia was moderate with CXR and could be improved when mlCXR was used for image interpretation.

scholarly journals Deep Learning Based Post-Process Correction of the Aerosol Parameters in the High-Resolution Sentinel-3 Level-2 Synergy Product

2021 ◽  
Author(s):  
Antti Lipponen ◽  
Jaakko Reinvall ◽  
Arttu Väisänen ◽  
Henri Taskinen ◽  
Timo Lähivaara ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data ◽  
Approximation Error ◽  
Model Parameters ◽  
Post Processing ◽  
Post Process ◽  
Processing Step ◽  
Aerosol Parameters ◽  
Level 2

Abstract. Satellite-based aerosol retrievals provide global spatially distributed estimates of atmospheric aerosol parameters that are commonly needed in applications such as estimation of atmospherically corrected satellite data products, climate modeling and air quality monitoring. However, a common feature of the conventional satellite aerosol retrievals is that they have reasonably low spatial resolution and poor accuracy caused by uncertainty in auxiliary model parameters, such as fixed aerosol model parameters, and the approximate forward radiative transfer models utilized to keep the computational complexity feasible. As a result, the improvement and re-processing of the operational satellite data retrieval algorithms would become a tedious and computationally excessive problem. To overcome these problems, we have developed a machine learning-based post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite retrieval data and a post-processing step where a machine learning algorithm is utilized to predict the approximation error in the conventional retrieval. With approximation error we refer to the discrepancy between the true aerosol parameters and the ones retrieved using the satellite data. Our hypothesis is that the prediction of the approximation error with a finite training data set is a less complex and easier task than the direct fully learned machine learning based prediction in which the aerosol parameters are directly predicted given the satellite observations and measurement geometry. With our approach, there is no need to re-run the existing retrieval algorithms and only a computationally feasible post-processing step is needed. Our approach is based on neural networks trained based on collocated satellite data and accurate ground based AERONET aerosol data. Based on our post-processing approach, we propose a post-process corrected high resolution Sentinel-3 Synergy aerosol product, which gives a spectral estimate of the aerosol optical depth at five different wavelengths with a high spatial resolution equivalent to the native resolution of the Sentinel-3 level-1 data (300 meters at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with better accuracy than the operational Sentinel-3 level-2 Synergy aerosol product or a conventional fully learned machine learning approach.

scholarly journals Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Amir Ahmad ◽  
Ourooj Safi ◽  
Sharaf Malebary ◽  
Sami Alesawi ◽  
Entisar Alkayal
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Polymerase Chain Reaction Test ◽  
Imbalanced Datasets ◽  
Polymerase Chain ◽  
Chain Reaction Test ◽  
Selection Of

The coronavirus disease 2019 (Covid-19) pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms were applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.

scholarly journals Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

2020 ◽  
Author(s):  
Thomas Langer ◽  
Martina Favarato ◽  
Riccardo Giudici ◽  
Gabriele Bassi ◽  
Roberta Garberi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Severe Acute Respiratory Syndrome ◽  
Clinical Data ◽  
Laboratory Data ◽  
Learning System ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Long Time ◽  
Polymerase Chain

Abstract Objective: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our maingoal was assessing the accuracy of artificial intelligence in forecasting the resultsof RT-PCR for SARS-COV-2, using basic information at hand in all emergencydepartments.Methods: This is a retrospective study carried out between February 22 and March 16 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2.Patients under 12 years old, with no leukocyte formula performed in the ED,were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission.Results: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed,on a larger-scale study, this approach could have important clinical and organizational implications.

scholarly journals Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12073
Author(s):  
Indira Mikkili ◽  
Abraham Peele Karlapudi ◽  
T. C. Venkateswarulu ◽  
Vidya Prabhakar Kodali ◽  
Deepika Sri Singh Macamdas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Imaging Techniques ◽  
Learning Tools ◽  
Rt Pcr ◽  
Drug Molecules ◽  
Potential Applications ◽  
The Neural Networks ◽  
Polymerase Chain ◽  
The Internet Of Things

The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted.

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