scholarly journals An Individualized Algorithm to Predict Mortality in COVID-19 Pneumonia: a Machine Learning Based Study

2022 ◽  
Author(s):  
Maria Elena Laino ◽  
Elena Generali ◽  
Tobia Tommasini ◽  
Giovanni Angelotti ◽  
Alessio Aghemo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Risk Score ◽  
Machine Learning Techniques ◽  
Clinical Settings ◽  
Risk Of Mortality ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Importance Analysis ◽  
High Level

IntroductionIdentifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow to analyze big amount of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning.Material and methodsWe conducted a retrospective cohort study on hospitalized adults COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach on vital parameters, laboratory values, and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation.Results1,135 consecutive patients (median age 70 years, 64% males) were enrolled, 48 patients were excluded, the cohort was randomly divided in training (760) and test (327). During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85, ±0.025), and three levels were defined that correlated well with in-hospital mortality.ConclusionsMachine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Selecting optimal SpMV realizations for GPUs via machine learning

2021 ◽  
pp. 109434202199073
Author(s):  
Ernesto Dufrechou ◽  
Pablo Ezzatti ◽  
Enrique S Quintana-Ortí
Keyword(s):  
Machine Learning ◽  
Sparse Matrix ◽  
Machine Learning Techniques ◽  
Optimal Method ◽  
Learning Techniques ◽  
General Rules ◽  
Machine Learning Approach ◽  
The Matrix ◽  
Time And Energy ◽  
Matrix Vector

More than 10 years of research related to the development of efficient GPU routines for the sparse matrix-vector product (SpMV) have led to several realizations, each with its own strengths and weaknesses. In this work, we review some of the most relevant efforts on the subject, evaluate a few prominent routines that are publicly available using more than 3000 matrices from different applications, and apply machine learning techniques to anticipate which SpMV realization will perform best for each sparse matrix on a given parallel platform. Our numerical experiments confirm the methods offer such varied behaviors depending on the matrix structure that the identification of general rules to select the optimal method for a given matrix becomes extremely difficult, though some useful strategies (heuristics) can be defined. Using a machine learning approach, we show that it is possible to obtain unexpensive classifiers that predict the best method for a given sparse matrix with over 80% accuracy, demonstrating that this approach can deliver important reductions in both execution time and energy consumption.

Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach

2021 ◽  
Author(s):  
Gediminas Adomavicius ◽  
Yaqiong Wang
Keyword(s):  
Machine Learning ◽  
General Purpose ◽  
Reliability Estimation ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Learning Techniques ◽  
Reliability Indicator ◽  
Machine Learning Approach ◽  
Prediction Reliability

Numerical predictive modeling is widely used in different application domains. Although many modeling techniques have been proposed, and a number of different aggregate accuracy metrics exist for evaluating the overall performance of predictive models, other important aspects, such as the reliability (or confidence and uncertainty) of individual predictions, have been underexplored. We propose to use estimated absolute prediction error as the indicator of individual prediction reliability, which has the benefits of being intuitive and providing highly interpretable information to decision makers, as well as allowing for more precise evaluation of reliability estimation quality. As importantly, the proposed reliability indicator allows the reframing of reliability estimation itself as a canonical numeric prediction problem, which makes the proposed approach general-purpose (i.e., it can work in conjunction with any outcome prediction model), alleviates the need for distributional assumptions, and enables the use of advanced, state-of-the-art machine learning techniques to learn individual prediction reliability patterns directly from data. Extensive experimental results on multiple real-world data sets show that the proposed machine learning-based approach can significantly improve individual prediction reliability estimation as compared with a number of baselines from prior work, especially in more complex predictive scenarios.

A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

2020 ◽  
Vol 2674 (10) ◽  
pp. 68-78
Author(s):  
Zhao Zhang ◽  
Yun Yuan ◽  
Xianfeng (Terry) Yang
Keyword(s):  
Machine Learning ◽  
Flow Model ◽  
Hybrid Approach ◽  
Traffic Monitoring ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Freeway Traffic ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Traffic Speed Estimation

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

Mining for Creativity: Determining the Creativity of Ideas Through Data Mining Techniques

2017 ◽  
Author(s):  
Christine A. Toh ◽  
Elizabeth M. Starkey ◽  
Conrad S. Tucker ◽  
Scarlett R. Miller
Keyword(s):  
Machine Learning ◽  
Design Research ◽  
Machine Learning Techniques ◽  
Numeric Model ◽  
Design Creativity ◽  
Large Sets ◽  
Learning Techniques ◽  
Ideation Methods ◽  
High Level ◽  
Design Ideas

The emergence of ideation methods that generate large volumes of early-phase ideas has led to a need for reliable and efficient metrics for measuring the creativity of these ideas. However, existing methods of human judgment-based creativity assessments, as well as numeric model-based creativity assessment approaches suffer from low reliability and prohibitive computational burdens on human raters due to the high level of human input needed to calculate creativity scores. In addition, there is a need for an efficient method of computing the creativity of large sets of design ideas typically generated during the design process. This paper focuses on developing and empirically testing a machine learning approach for computing design creativity of large sets of design ideas to increase the efficiency and reliability of creativity evaluation methods in design research. The results of this study show that machine learning techniques can predict creativity of ideas with relatively high accuracy and sensitivity. These findings show that machine learning has the potential to be used for rating the creativity of ideas generated based on their descriptions.

scholarly journals Delivering Precision Medicine to Patients with Spinal Cord Disorders; Insights into Applications of Bioinformatics and Machine Learning from Studies of Degenerative Cervical Myelopathy

2021 ◽  
Author(s):  
Kalum J. Ost ◽  
David W. Anderson ◽  
David W. Cadotte
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
New Technologies ◽  
Machine Learning Techniques ◽  
Massive Datasets ◽  
Learning Framework ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Spinal Cord Disorders ◽  
Degenerative Cervical Myelopathy

With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.

scholarly journals Machine Learning for Epidemiological Analysis in The Industrial Area for a Sustainable Life

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1017-1025
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Industrial Area ◽  
Epidemiological Studies ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Air Pollution Exposure ◽  
Pollution Exposure

Pollution exposure and human health in the industry contaminated area are always a concern. The need for industrialization urges to concentrate on sustainable life of residents in the vicinity of the industrial area rather than opposing the industrialists. Literature in epidemiological studies reveal that air pollution is one of the major problems for health risks faced by residents in the industrial area. Main pollutants in industry related air pollution are particulate matter (PM2.5, PM10), SO2 , NO2 , and other pollutants upon the industry. Data for epidemiological studies obtained from different sources which are limited to public access include residents’ sociodemographic characters, health problems, and air quality index for personal exposure to pollutants. This combined data and limited resources make the analysis more complex so that statistical methods cannot compensate. Our review finds that there is an increase in literature that evaluates the connection between ambient air pollution exposure and associated health events of residents in the industrially polluted area using statistical methods, mainly regression models. A very few applies machine learning techniques to figure out the impact of common air pollution exposure on human health. Most of the machine learning approach to epidemiological studies end up in air pollution exposure monitoring, not to correlate its association with diseases. A machine learning approach to epidemiological studies can automatically characterize the residents’ exposure to pollutants and its associated health effects. Uniqueness of the model depends on the appropriate exhaustive data that characterizes the features, and machine learning algorithm used to build the model. In this contribution, we discuss various existing approaches that evaluate residents’ health effects and the source of irritation in association with air pollution exposure, focuses machine learning techniques and mathematical background for epidemiological studies for residents’ sustainable life.

scholarly journals Detection of Security Attacks in Industrial IoT Networks: A Blockchain and Machine Learning Approach

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2662
Author(s):  
Henry Vargas ◽  
Carlos Lozano-Garzon ◽  
Germán A. Montoya ◽  
Yezid Donoso
Keyword(s):  
Machine Learning ◽  
Information Transfer ◽  
Machine Learning Techniques ◽  
Protection Mechanism ◽  
Secure Information ◽  
Learning Techniques ◽  
Industrial Iot ◽  
Machine Learning Approach ◽  
Detection Mechanisms ◽  
Viable Mechanism

Internet of Things (IoT) networks have been integrated into industrial infrastructure schemes, positioning themselves as devices that communicate highly classified information for the most critical companies of world nations. Currently, and in order to look for alternatives to mitigate this risk, solutions based on Blockchain algorithms and Machine Learning techniques have been implemented separately with the aim of mitigating potential threats in IIoT networks. In this paper, we sought to integrate the previous solutions to create an integral protection mechanism for IoT device networks, which would allow the identification of threats, activate secure information transfer mechanisms, and it would be adapted to the computational capabilities of industrial IoT. The proposed solution achieved the proposed objectives and is presented as a viable mechanism for detecting and containing intruders in an IoT network. In some cases, it overcomes traditional detection mechanisms such as an IDS.

scholarly journals Review on Machine Learning Techniques for Diagnosis of Heart Disease

2021 ◽  
Vol 9 (8) ◽  
pp. 1418-1420
Author(s):  
Harshal P. Sabale
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Human Health ◽  
World Health Organisation ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Heart Disease Diagnosis

Abstract: Now-a-days, heart disease is becoming a concern to human health. According to World Health organisation (WHO), heart disease is the number one killer among other fatal diseases. Excessive smoking, alcohol consumption and junk food are culprit for the heart disease. Physical inactivity is also a concerning to the human health. Heart disease is pretty hard to predict or diagnose using traditional methods like counselling. But, now-a-days, medical fields are using machine learning to predict or diagnose different diseases. Implementation of machine learning techniques provides faster and mostly accurate results. This can save many life. In this paper, different machine learning approach for heart disease diagnosis are reviewed. Keywords: Heart disease, CVD, Machine Learning

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