scholarly journals Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant, Evidence from the Biomedical Literature: A Systematic Review (Preprint)

10.2196/30401 ◽  
2021 ◽  
Author(s):  
Wael Abdelkader ◽  
Tamara Navarro ◽  
Rick Parrish ◽  
Chris Cotoi ◽  
Federico Germini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Biomedical Literature ◽  
Learning Approaches ◽  
High Quality ◽  
Relevant Evidence

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant, Evidence from the Biomedical Literature: A Systematic Review (Preprint)

2021 ◽  
Author(s):  
Wael Abdelkader ◽  
Tamara Navarro ◽  
Rick Parrish ◽  
Chris Cotoi ◽  
Federico Germini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Strong Evidence ◽  
Clinical Care ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
High Quality ◽  
Applied Machine Learning

BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE To summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Applying machine learning to distinguish studies with strong evidence for clinical care has the potential to decrease the workload of manually identifying these. The evidence base is active and evolving. Reported methods were variable across the studies but focused on supervised machine learning approaches. Performance may improve by applying more sophisticated approaches such as active learning, auto-machine learning, and unsupervised machine learning approaches.

Auto Machine Learning Approaches to Refine the Identification of High-Quality Clinical Research Articles from The Biomedical Literature: A Study Protocol (Preprint)

2021 ◽  
Author(s):  
Wael Abdelkader ◽  
Tamara Navarro ◽  
Rick Parrish ◽  
Chris Cotoi ◽  
Federico Germini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
High Sensitivity ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Learning Models ◽  
High Quality ◽  
Search Filters ◽  
Machine Learning Models

UNSTRUCTURED Due to the continued rapid growth in published biomedical literature, it is increasingly difficult to identify and retrieve high-quality evidence. Machine learning approaches have been applied to address this issue. Some models developed using supervised machine learning approaches have achieved high sensitivity or recall, however precision has been variable. In a series of experiments, we will assess the performance of machine learning models to retrieve high-quality, high relevance evidence for clinical consideration from the biomedical literature. The models will be trained using an automated approach applied to a database of almost 100, 000 articles that have been tagged by highly trained research staff based on criteria for high-quality and assessed for clinical relevance by clinicians. We will evaluate and report on the effects of various classifiers, preprocessing steps, feature selection, and the use of balanced vs unbalanced datasets applied during model development on the performance of the derived supervised machine learning models. The series was devised to improve the precision of the retrieval of high-quality articles by applying a machine learning classifier sequentially after using high sensitivity Boolean search filters to an ongoing literature surveillance process. Our multi-level analysis of the various steps of machine learning model development will help expand the existing knowledge base on the effect of each step on the performance of machine learning models.

scholarly journals Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches

2019 ◽  
Vol 68 ◽  
pp. 285-299 ◽  
Author(s):  
Vahid Farrahi ◽  
Maisa Niemelä ◽  
Maarit Kangas ◽  
Raija Korpelainen ◽  
Timo Jämsä
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Approaches ◽  
Activity Monitors ◽  
Calibration And Validation

scholarly journals Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

2020 ◽  
Vol 130 ◽  
pp. 109899 ◽  
Author(s):  
Ioannis Antonopoulos ◽  
Valentin Robu ◽  
Benoit Couraud ◽  
Desen Kirli ◽  
Sonam Norbu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Energy Demand ◽  
Learning Approaches ◽  
Demand Side ◽  
Demand Side Response

scholarly journals Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices—A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6345
Author(s):  
Floriant Labarrière ◽  
Elizabeth Thomas ◽  
Laurine Calistri ◽  
Virgil Optasanu ◽  
Mathieu Gueugnon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Learning Algorithms ◽  
Assistive Devices ◽  
Machine Learning Algorithms ◽  
Assistive Device ◽  
Learning Approaches ◽  
Study Characteristics ◽  
Locomotion Mode ◽  
Meta Analyses

Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.

scholarly journals A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

2021 ◽  
Vol 9 (10) ◽  
pp. 1410-1440
Author(s):  
T Heena Fayaz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Networks ◽  
Social Media ◽  
Future Research ◽  
Learning Approaches ◽  
Electoral Campaigns ◽  
Regression Methods ◽  
New Research

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

scholarly journals SciReader: A Cloud-based Recommender System for Biomedical Literature

2018 ◽  
Author(s):  
Priya Desai ◽  
Natalie Telis ◽  
Ben Lehmann ◽  
Keith Bettinger ◽  
Jonathan K. Pritchard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Topic Modeling ◽  
Learning Algorithms ◽  
Relevant Literature ◽  
Machine Learning Algorithms ◽  
Biomedical Literature ◽  
High Quality ◽  
Link Type ◽  
Personalized Recommender System

AbstractWith the growing number of biomedical papers published each year, keeping up with relevant literature has become increasingly important, and yet more challenging. SciReader (www.scireader.com) is a cloud-based personalized recommender system that specifically aims to assist biomedical researchers and clinicians identify publications of interest to them. SciReader uses topic modeling and other machine learning algorithms to provide users with recommendations that are recent, relevant, and of high quality1.

scholarly journals A SYSTEMATIC REVIEW METHOD FOR SECURITY ANALYSIS OF INTERNET OF THINGS ON HONEYPOT DETECTION

TEKNOKOM ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 16-20
Author(s):  
Andrie Yuswanto ◽  
Budi Wibowo
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Internet Of Things ◽  
Security Analysis ◽  
The Internet ◽  
Malware Analysis ◽  
Learning Approaches ◽  
Exclusion Criteria ◽  
Recent Approach ◽  
The Internet Of Things

A very significant increase in the spread of malware has resulted in malware analysis. A recent approach to using the internet of things has been put forward by many researchers. Iot tool learning approaches as a more effective and efficient approach to dealing with malware compared to conventional approaches. At the same time, the researchers transformed the honeypot as a device capable of gathering malware information. The honeypot is designed as a malware trap and is stored on the provided system. Then log the managed events and gather information about the activity and identity of the attacker. This paper aims to use a honeypot in machine learning to deal with malware The Systematic Literature Review (SLR) method was used to identify 207. Then 10 papers were selected to be investigated based on inclusion and exclusion criteria. . The technique used by most researchers is to utilize the available honeypot dataset. Meanwhile, based on the type of malware being analyzed, honeypot in machine learning is mostly used to collect IoT-based malware.

scholarly journals Artificial intelligence: A cutting edge technology in agriculture

2021 ◽  
Vol 17 (1) ◽  
pp. 114-120
Author(s):  
Sidhant Allawadi ◽  
Jayaty ◽  
Parmod Sharma ◽  
Kapil Rohilla ◽  
Gopal Deokar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Computer Vision ◽  
Cutting Edge ◽  
High Quality ◽  
High Expectations ◽  
Advanced Technologies ◽  
The Common ◽  
Smart Technologies

Attention is currently being paid to the use of smart technologies. Agriculture has provided an important source of food for humans over thousands of years, including the development of appropriate farming methods for the cultivation of different crops. The emergence of new advanced technologies has the potential to monitor the agricultural environment to ensure high-quality produce. In this context, a systematic review that aimsto study the application of various technologies and algorithms in Artificial Intelligence (AI) with the latest solutions to make the farming more efficient remains one of the greatest imperatives. Artificial intelligence can be applied directly in the field of agriculture for various operations. Amid high expectations about how AI will help the common personand transform his mindset, thoughts and attitude towards the benefits that it may bring. There are certain concerns about the ill effects of such sophisticated technologies as well.This review also focuses on the activation of perceptive technologies and application of computer vision and machine learning in agriculture.

Machine learning approaches and their current application in plant molecular biology: A systematic review

Plant Science ◽  
2019 ◽  
Vol 284 ◽  
pp. 37-47 ◽  
Author(s):  
Jose Cleydson F. Silva ◽  
Ruan M. Teixeira ◽  
Fabyano F. Silva ◽  
Sergio H. Brommonschenkel ◽  
Elizabeth P.B. Fontes
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Molecular Biology ◽  
Plant Molecular Biology ◽  
Learning Approaches ◽  
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