scholarly journals Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

2020 ◽  
Vol 2 (10) ◽  
Author(s):  
Sanaz Tayefeh Hashemi ◽  
Omid Mahdi Ebadati ◽  
Harleen Kaur
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Cost Estimation ◽  
Construction Projects ◽  
Machine Learning Techniques ◽  
Estimation And Prediction ◽  
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

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

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.

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