scholarly journals Collection, Processing and Analysis of Heterogeneous Data Coming from Spanish Hospitals in the Context of COVID-19

2021 ◽  
Author(s):  
Marta Barroso ◽  
Adrián Tormos ◽  
Raquel Pérez-Arnal ◽  
Sergio Alvarez-Napagao ◽  
Dario Garcia-Gasulla
Keyword(s):  
Machine Learning ◽  
Clinical Studies ◽  
Medical Records ◽  
Data Entry ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Publicly Funded ◽  
Combined Analysis ◽  
Learning Techniques

The COVID-19 pandemic has already caused more than 150,000,000 cases worldwide. In Spain this has lead to a massive and simultaneous saturation of all sanitary regions. Coherently, the quick and consistent understanding of the COVID-19 disease requires of the combined analysis of thousands of medical records generated by dozens of different institutions. In the context of the publicly funded CIBERES-UCI-COVID project, we have gathered, cleaned and preprocessed data from heterogeneous sources – more than 30 hospitals, with different data entry systems – in order to produce a unified database, of more than 6.000 patients, that is used in several clinical studies being carried by different multidisciplinary groups. In this paper, we identify the complexities we encountered, the solutions we applied, and we summarise the statistical and machine learning techniques we have applied for the studies.

scholarly journals Crop Management with the IoT: An Interdisciplinary Survey

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 181 ◽  
Author(s):  
Giuliano Vitali ◽  
Matteo Francia ◽  
Matteo Golfarelli ◽  
Maurizio Canavari
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Management ◽  
Cropping System ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Crop Fields ◽  
Learning Techniques ◽  
The Internet Of Things

In this study, we analyze how crop management will benefit from the Internet of Things (IoT) by providing an overview of its architecture and components from agronomic and technological perspectives. The present analysis highlights that IoT is a mature enabling technology with articulated hardware and software components. Cheap networked devices can sense crop fields at a finer grain to give timeliness warnings on the presence of stress conditions and diseases to a wider range of farmers. Cloud computing allows reliable storage, access to heterogeneous data, and machine-learning techniques for developing and deploying farm services. From this study, it emerges that the Internet of Things will draw attention to sensor quality and placement protocols, while machine learning should be oriented to produce understandable knowledge, which is also useful to enhance cropping system simulation systems.

scholarly journals Application of Machine Learning in Rhinology: A State of the Art Review

2020 ◽  
Vol 63 (8) ◽  
pp. 341-349
Author(s):  
Myeong Sang Yu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Records ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Machine Learning Applications

The revolutionary development of artificial intelligence (AI) such as machine learning and deep learning have been one of the most important technology in many parts of industry, and also enhance huge changes in health care. The big data obtained from electrical medical records and digitalized images accelerated the application of AI technologies in medical fields. Machine learning techniques can deal with the complexity of big data which is difficult to apply traditional statistics. Recently, the deep learning techniques including convolutional neural network have been considered as a promising machine learning technique in medical imaging applications. In the era of precision medicine, otolaryngologists need to understand the potentialities, pitfalls and limitations of AI technology, and try to find opportunities to collaborate with data scientists. This article briefly introduce the basic concepts of machine learning and its techniques, and reviewed the current works on machine learning applications in the field of otolaryngology and rhinology.

scholarly journals Relative contributions of Shakespeare and Fletcher in Henry VIII: An analysis based on most frequent words and most frequent rhythmic patterns

2020 ◽  
Author(s):  
Petr Plecháč
Keyword(s):  
Machine Learning ◽  
William Shakespeare ◽  
Collaborative Work ◽  
Machine Learning Techniques ◽  
Henry Viii ◽  
John Fletcher ◽  
Combined Analysis ◽  
Learning Techniques ◽  
New Evidence

Abstract The versified play Henry VIII is nowadays widely recognized to be a collaborative work not written solely by William Shakespeare. We employ combined analysis of vocabulary and versification together with machine learning techniques to determine which other authors took part in the writing of the play and what were their relative contributions. Unlike most previous studies, we go beyond the attribution of particular scenes and use the rolling attribution approach to determine the probabilities of authorship of pieces of texts, without respecting the scene boundaries. Our results highly support the canonical division of the play between William Shakespeare and John Fletcher proposed by James Spedding, but also bring new evidence supporting the modifications proposed later by Thomas Merriam.

scholarly journals A review on prediction of diabetes type 2 by machine learning techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 5638-5651
Author(s):  
Alka Singh
Keyword(s):  
Machine Learning ◽  
Diabetes Type 2 ◽  
Relevant Information ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Information Usage ◽  
Learning Techniques ◽  
Prediction Of Diabetes

Machine learning is considered to be one of the most promising tools when it comes to working with heterogeneous data. It provides a new dimension which enables one to extract relevant data and take decision for the effective functioning of the network, making use of network generated data. Every sphere of our life is now dependent on machine learning. It has flourished in every dimension. Making it versatile and ever demanding. Department of healthcare contains very abundant and sensitive information which is needed to be carefully handled. Diabetes mellitus is increasing exponentially and is spreading like anything in the world. A reliable prediction system should be present for diagnosing diabetes. Variety of machine learning techniques find their use in the examination of data from variant perspectives and summarizing it into effective information. Usage of new patterns is done to elucidate these patterns in order to deliver relevant information for their users. By making use of techniques such as SVM, random forest, logistic regression, naïve bayes etc the prediction of diabetes can be done easily and accurately. In this study we will make use of different machine learning techniques and try to find accurate prediction regarding the same.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Sign in / Sign up

Export Citation Format

Share Document