A machine learning algorithm for sorting online comments via topic modeling

2021 ◽  
Vol 9 (2) ◽  
pp. 4-14
Author(s):  
Junzhe Zhu ◽  
Elizabeth Wickes ◽  
John R. Gallagher
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Linear Time ◽  
Professional Communication ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
Area Of Interest ◽  
Online Comments ◽  
Technical And Professional Communication ◽  
Display Systems

This article uses a machine learning algorithm to demonstrate a proof-of-concept case for moderating and managing online comments as a form of content moderation, which is an emerging area of interest for technical and professional communication (TPC) researchers. The algorithm sorts comments by topical similarity to a reference comment/article rather than display comments by linear time and popularity. This approach has the practical benefit of enabling TPC researchers to reconceptualize content display systems in dynamic ways.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

Machine learning algorithm for the diagnosis of pulmonary embolism: a proof of concept study

2020 ◽  
Author(s):  
Konstantinos Exarchos ◽  
Agapi Aggelopoulou ◽  
Konstantinos Bartziokas ◽  
Elpida Tsina ◽  
Christos Tagkas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pulmonary Embolism ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
Concept Study

scholarly journals A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1132
Author(s):  
Youjin Kim ◽  
Yunsoo Kim ◽  
Jiyoung Hwang ◽  
Tim J. van den Broek ◽  
Bumjo Oh ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Healthy Population ◽  
Complex Data ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
Concept Study ◽  
Cohort Data ◽  
Oxidative Stress Status

Oxidative stress aggravates the progression of lifestyle-related chronic diseases. However, knowledge and practices that enable quantifying oxidative stress are still lacking. Here, we performed a proof-of-concept study to predict the oxidative stress status in a healthy population using retrospective cohort data from Boramae medical center in Korea (n = 1328). To obtain binary performance measures, we selected healthy controls versus oxidative disease cases based on the “health space” statistical methodology. We then developed a machine learning algorithm for discrimination of oxidative stress status using least absolute shrinkage and selection operator (LASSO)/elastic net regression with 10-fold cross-validation. A proposed fine-tune model included 16 features out of the full spectrum of diverse and complex data. The predictive performance was externally evaluated by generating receiver operating characteristic curves with area under the curve of 0.949 (CI 0.925 to 0.974), sensitivity of 0.923 (CI 0.879 to 0.967), and specificity of 0.855 (CI 0.795 to 0.915). Moreover, the discrimination power was confirmed by applying the proposed diagnostic model to the full dataset consisting of subjects with various degrees of oxidative stress. The results provide a feasible approach for stratifying the oxidative stress risks in the healthy population and selecting appropriate strategies for individual subjects toward implementing data-driven precision nutrition.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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