Using Machine Learning to support health system planning during the Covid-19 pandemic: a case study using data from São José dos Campos (Brazil)

2021 ◽  
Vol 24 (3) ◽  
Author(s):  
Leila Abuabara ◽  
Maria Gabriela Valeriano ◽  
Carlos Roberto Veiga Kiffer ◽  
Horácio Hideki Yanasse ◽  
Ana Carolina Lorena
Keyword(s):  
Machine Learning ◽  
Population Characteristics ◽  
Machine Learning Techniques ◽  
System Planning ◽  
Health System Planning ◽  
Learning Techniques ◽  
Using Data ◽  
The City ◽  
Support Decision Making

Many efforts were made by the scientific community during the Covid-19 pandemic to understand the disease and better manage health systems' resources. Believing that city and population characteristics influence how the disease spreads and develops, we used Machine Learning techniques to provide insights to support decision-making in the city of São José dos Campos (SP), Brazil. Using a database with information from people who undergo the Covid-19 test in this city, we generate and evaluate predictive models related to severity, need for hospitalization and period of hospitalization. Additionally, we used the SHAP value for models' interpretation of the most decisive attributes influencing the predictions. We can conclude that patient age linked to symptoms such as saturation and respiratory distress and comorbidities such as cardiovascular disease and diabetes are the most important factors to consider when one wants to predict severity and need for hospitalization in this city. We also stress the need of a greater attention to the proper collection of this information from citizens who undergo the Covid-19 diagnosis test.

Modeling Relevance Relations Using Machine Learning Techniques

2011 ◽  
pp. 168-207
Author(s):  
Jelber Sayyad Shirabad ◽  
Timothy C. Lethbridge ◽  
Stan Matwin
Keyword(s):  
Machine Learning ◽  
Empirical Evaluation ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Software Engineers ◽  
Field Deployment ◽  
Complex Relationships ◽  
Using Data

This chapter presents the notion of relevance relations, an abstraction to represent relationships between software entities. Relevance relations map tuples of software entities to values that reflect how related they are to each other. Although there are no clear definitions for these relationships, software engineers can typically identify instances of these complex relationships. We show how a classifier can model a relevance relation. We also present the process of creating such models by using data mining and machine learning techniques. In a case study, we applied this process to a large legacy system; our system learned models of a relevance relation that predict whether a change in one file may require a change in another file. Our empirical evaluation shows that the predictive quality of such models makes them a viable choice for field deployment. We also show how by assigning different misclassification costs such models can be tuned to meet the needs of the user in terms of their precision and recall.

Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam

2021 ◽  
Author(s):  
Chinh Luu ◽  
Quynh Duy Bui ◽  
Romulus Costache ◽  
Luan Thanh Nguyen ◽  
Thu Thuy Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Prone Area ◽  
Flood Prone Area

Machine Learning Assisted Interpretation: Integrated Fault Prediction Extraction Case Study from the Groningen Gas Field, Netherlands

2021 ◽  
pp. 1-67
Author(s):  
Stewart Smith ◽  
Olesya Zimina ◽  
Surender Manral ◽  
Michael Nickel
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Seismic Data ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Gas Field ◽  
Seismic Fault ◽  
Learning Techniques ◽  
Groningen Gas Field

Seismic fault detection using machine learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and de-risking activities. Efforts to automate the detection and extraction of geological features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, NE Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D pre-stack depth migrated seismic dataset. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis (PCA) to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geological context to the interpreter and input to dependent workflows more efficiently.

scholarly journals Leveraging Business Data Analytics and Machine Learning Techniques for Competitive Advantage: Case Study Evidence from Small Businesses

2021 ◽  
Vol 2 (1) ◽  
pp. 73-87
Author(s):  
Rathimala Kannan ◽  
Intan Soraya Rosdi ◽  
Kannan Ramakrishna ◽  
Haziq Riza Abdul Rasid ◽  
Mohamed Haryz Izzudin Mohamed Rafy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Competitive Advantage ◽  
Small Businesses ◽  
Data Analytics ◽  
Research Question ◽  
Machine Learning Techniques ◽  
Multiple Sources ◽  
Learning Techniques ◽  
Business Data

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

Homeland Security Data Mining and Link Analysis

2011 ◽  
pp. 566-569
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Homeland Security ◽  
Link Analysis ◽  
Machine Learning Techniques ◽  
Data Mining Technique ◽  
Learning Techniques ◽  
Terrorist Groups ◽  
Using Data ◽  
Terrorist Events

Data mining is the process of posing queries to large quantities of data and extracting information often previously unknown using mathematical, statistical, and machine-learning techniques. Data mining has many applications in a number of areas, including marketing and sales, medicine, law, manufacturing, and, more recently, homeland security. Using data mining, one can uncover hidden dependencies between terrorist groups as well as possibly predict terrorist events based on past experience. One particular data-mining technique that is being investigated a great deal for homeland security is link analysis, where links are drawn between various nodes, possibly detecting some hidden links.

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