Prediction of COVID-19 prognosis using machine learning techniques: a systematic review v1 (protocols.io.bu3enyje)

protocols.io ◽  
2021 ◽  
Author(s):  
Alexandre Negrao ◽  
Carolina Sant' ◽  
Larissa Braga ◽  
Luiza Coimbra ◽  
Renata Araujo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Techniques ◽  
Learning Techniques

Applicability of machine learning techniques in food intake assessment: A systematic review

2021 ◽  
pp. 1-18
Author(s):  
Larissa Oliveira Chaves ◽  
Ana Luiza Gomes Domingos ◽  
Daniel Louzada Fernandes ◽  
Fabio Ribeiro Cerqueira ◽  
Rodrigo Siqueira-Batista ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Food Intake ◽  
Machine Learning Techniques ◽  
Intake Assessment ◽  
Learning Techniques

scholarly journals A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT)

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6100
Author(s):  
Vibhuti Gupta ◽  
Thomas M. Braun ◽  
Mosharaf Chowdhury ◽  
Muneesh Tewari ◽  
Sung Won Choi
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cell Transplantation ◽  
Hematopoietic Cell Transplantation ◽  
Hematopoietic Cell ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Hematopoietic Stem ◽  
Curative Therapy ◽  
Learning Techniques

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

scholarly journals Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Amina Adadi ◽  
Safae Adadi ◽  
Mohammed Berrada
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Modern Medicine ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Statistical Tool ◽  
Learning Techniques ◽  
Critical Issues ◽  
Scope Of Application ◽  
Quo Vadis

Machine learning has undergone a transition phase from being a pure statistical tool to being one of the main drivers of modern medicine. In gastroenterology, this technology is motivating a growing number of studies that rely on these innovative methods to deal with critical issues related to this practice. Hence, in the light of the burgeoning research on the use of machine learning in gastroenterology, a systematic review of the literature is timely. In this work, we present the results gleaned through a systematic review of prominent gastroenterology literature using machine learning techniques. Based on the analysis of 88 journal articles, we delimit the scope of application, we discuss current limitations including bias, lack of transparency, accountability, and data availability, and we put forward future avenues.

scholarly journals Performance Forecasting of Share Market using Machine Learning Techniques: A Review

2016 ◽  
Vol 6 (6) ◽  
pp. 3196 ◽  
Author(s):  
Sachin Kamley ◽  
Shailesh Jaloree ◽  
R.S. Thakur
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Share Price ◽  
Machine Learning Techniques ◽  
Factors Affecting ◽  
Price Prediction ◽  
Forecasting Methods ◽  
Learning Techniques ◽  
Enormous Amount ◽  
Performance Forecasting

<p>Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.</p>

scholarly journals Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

2019 ◽  
Vol Volume 15 ◽  
pp. 1605-1627 ◽  
Author(s):  
Renato de Filippis ◽  
Elvira Anna Carbone ◽  
Raffaele Gaetano ◽  
Antonella Bruni ◽  
Valentina Pugliese ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Functional Mri ◽  
Machine Learning Techniques ◽  
Diagnostic Approach ◽  
Learning Techniques

The use of machine learning techniques in trauma-related disorders: a systematic review

2020 ◽  
Vol 121 ◽  
pp. 159-172 ◽  
Author(s):  
Luis Francisco Ramos-Lima ◽  
Vitoria Waikamp ◽  
Thyago Antonelli-Salgado ◽  
Ives Cavalcante Passos ◽  
Lucia Helena Machado Freitas
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Techniques ◽  
Learning Techniques
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