Machine learning methods applied to triage in emergency services: A systematic review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

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A systematic review on machine learning in sellar region diseases: quality and reporting items

Endocrine Connections ◽

10.1530/ec-19-0156 ◽

2019 ◽

Vol 8 (7) ◽

pp. 952-960 ◽

Cited By ~ 8

Author(s):

Nidan Qiao

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Quality Assessment ◽

Assessment Tool ◽

Unmet Need ◽

Hormone Deficiency ◽

Pituitary Neoplasms ◽

Sellar Region ◽

Learning Methods ◽

Machine Learning Methods

Introduction Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies. Methods PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance. Results Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing’s disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results. Conclusions Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.

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Diagnose and Prediction of Different Machine Learning methods for Knee Osteoarthritis: a protocol for systematic review and meta-analysis

10.21203/rs.3.rs-903246/v1 ◽

2021 ◽

Author(s):

yu zhou ◽

Tong Mu ◽

Xiaochuan Kong ◽

Le Zhang

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Knee Osteoarthritis ◽

Meta Analysis ◽

Characteristic Curve ◽

Joint Disease ◽

Pool Data ◽

Learning Methods ◽

Machine Learning Methods ◽

Sensitivity Specificity

Abstract Background: Knee osteoarthritis (OA) is a chronic and progressive joint disease with a higher contributor to global disability, mainly in the elderly and particularly in women. The available diagnostic approaches such as X-ray, computed tomography and magnetic resonance imaging have large precision errors and low sensitivity. Machine learning (ML) is the application of probabilistic algorithms to train a computational model to make predictions, it has great potential to become a valuable clinical diagnostic tool. This review aims to determine the diagnosis and prediction accuracy of different machine learning methods for Knee Osteoarthritis Methods: Two reviewers systematically searched Cochrane, PubMed, EMBASE, and Web of Science (last updated in June 2020) for eligible articles. To identify potentially missed publications, the reference lists of the final included studies were manually screened. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC). We will use the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Two independent reviewers will conduct all procedures of study selection, data extraction, and methodological assessment. Any disagreements will be consulted with a third reviewer. RevMan 5.3 software and Stata V15.0 will be used to pool data and to carry out the meta-analysis if it is possible. Results: This systematic review will provide a high-quality synthesis of machine learning for diagnose of knee Osteoarthritis from various evaluation aspects including accuracy, sensitivity, specificity and AUC.Conclusion: The findings of this systematic review will provide latest evidence of diagnosis and prediction of different machine learning for patients with knee Osteoarthritis.Ethics and dissemination: No individual patient data will be used in this study; thus, no ethics approval is needed.Systematic review registration: PROSPERO CRD: 42019133305

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Application of Machine Learning Methods in Mental Health Detection: A Systematic Review

IEEE Access ◽

10.1109/access.2020.3029154 ◽

2020 ◽

Vol 8 ◽

pp. 183952-183964

Author(s):

Rohizah Abd Rahman ◽

Khairuddin Omar ◽

Shahrul Azman Mohd Noah ◽

Mohd Shahrul Nizam Mohd Danuri ◽

Mohammed Ali Al-Garadi

Keyword(s):

Mental Health ◽

Machine Learning ◽

Systematic Review ◽

Learning Methods ◽

Machine Learning Methods

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A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning

Applied Sciences ◽

10.3390/app10175811 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5811

Author(s):

Imatitikua D. Aiyanyo ◽

Hamman Samuel ◽

Heuiseok Lim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Domain Knowledge ◽

Supervised Machine Learning ◽

Detection Methods ◽

Data Sets ◽

Research Topics ◽

Research Papers ◽

Learning Methods ◽

Machine Learning Methods

This is a systematic review of over one hundred research papers about machine learning methods applied to defensive and offensive cybersecurity. In contrast to previous reviews, which focused on several fragments of research topics in this area, this paper systematically and comprehensively combines domain knowledge into a single review. Ultimately, this paper seeks to provide a base for researchers that wish to delve into the field of machine learning for cybersecurity. Our findings identify the frequently used machine learning methods within supervised, unsupervised, and semi-supervised machine learning, the most useful data sets for evaluating intrusion detection methods within supervised learning, and methods from machine learning that have shown promise in tackling various threats in defensive and offensive cybersecurity.

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Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0010056 ◽

2022 ◽

Vol 16 (1) ◽

pp. e0010056

Author(s):

Emmanuelle Sylvestre ◽

Clarisse Joachim ◽

Elsa Cécilia-Joseph ◽

Guillaume Bouzillé ◽

Boris Campillo-Gimenez ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Big Data ◽

Real World ◽

Data Sources ◽

Data Driven ◽

Real World Data ◽

Learning Methods ◽

Machine Learning Methods ◽

Dengue Prediction

Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

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Adverse drug reaction analysis using statistical and machine learning methods: A systematic review (Preprint)

10.2196/preprints.28214 ◽

2021 ◽

Author(s):

Yu Rang Park

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Adverse Drug Reaction ◽

Drug Reaction ◽

Meta Analysis ◽

Statistical Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

Multiple Data ◽

Current Systematic Review

UNSTRUCTURED An adverse drug reaction (ADR) is an unintended response induced by a drug. It is important to determine the association between drugs and ADRs. There are many methods to demonstrate this association. This systematic review aimed to examine the analysis tools by considering original articles that introduced statistical and machine learning methods for predicting ADRs in humans. A systematic literature review of EMBASE and PubMed was conducted based on articles published from January 2015 to March 2020. The keywords were statistical, machine learning, and deep learning methods for the detection of ADR signals in the title and abstract. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guidelines. In total, 72 articles were included in the current systematic review; of these, 51 and 21 addressed statistical and machine learning methods, respectively. This study provides a graphical overview of data-driven methods for detecting ADRs with multiple data sources for patient drug safety.

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Machine learning methods for toxic comment classification: a systematic review

Acta Universitatis Sapientiae Informatica ◽

10.2478/ausi-2020-0012 ◽

2020 ◽

Vol 12 (2) ◽

pp. 205-216

Author(s):

Darko Andročec

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Classification Problem ◽

Future Research ◽

Primary Study ◽

Learning Methods ◽

Data Set ◽

Machine Learning Methods ◽

Research Themes ◽

Evaluation Metric

Abstract Nowadays users leave numerous comments on different social networks, news portals, and forums. Some of the comments are toxic or abusive. Due to numbers of comments, it is unfeasible to manually moderate them, so most of the systems use some kind of automatic discovery of toxicity using machine learning models. In this work, we performed a systematic review of the state-of-the-art in toxic comment classification using machine learning methods. We extracted data from 31 selected primary relevant studies. First, we have investigated when and where the papers were published and their maturity level. In our analysis of every primary study we investigated: data set used, evaluation metric, used machine learning methods, classes of toxicity, and comment language. We finish our work with comprehensive list of gaps in current research and suggestions for future research themes related to online toxic comment classification problem.

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