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

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.

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

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

A survey on adverse drug reaction studies: data, tasks and machine learning methods

2019 ◽  
Author(s):  
Duc Anh Nguyen ◽  
Canh Hao Nguyen ◽  
Hiroshi Mamitsuka
Keyword(s):  
Machine Learning ◽  
Adverse Drug Reaction ◽  
Drug Reaction ◽  
Drug Discovery ◽  
Data Sources ◽  
Mechanism Analysis ◽  
Learning Methods ◽  
Open Problems ◽  
Machine Learning Methods ◽  
Benchmark Data

Abstract Motivation Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support analyzing and predicting ADRs. Nonetheless, there are still remaining challenges in ADR studies. Results In this paper, we summarized ADR data sources and review ADR studies in three tasks: drug-ADR benchmark data creation, drug–ADR prediction and ADR mechanism analysis. We focused on machine learning methods used in each task and then compare performances of the methods on the drug–ADR prediction task. Finally, we discussed open problems for further ADR studies. Availability Data and code are available at https://github.com/anhnda/ADRPModels.

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

2022 ◽  
Vol 60 ◽  
pp. 101109
Author(s):  
Rocío Sánchez-Salmerón ◽  
José L. Gómez-Urquiza ◽  
Luis Albendín-García ◽  
María Correa-Rodríguez ◽  
María Begoña Martos-Cabrera ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Emergency Services ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 778
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
Gonçalo Marques ◽  
Maria Vanessa Villasana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Automatic Recognition ◽  
Learning Methods ◽  
Less Invasive ◽  
Machine Learning Methods ◽  
The Future

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.

scholarly journals Predicting Fall Risk Through Automatic Wearable Monitoring

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Markey Olson ◽  
Thurmon Lockhart
Keyword(s):  
Machine Learning ◽  
Fall Risk ◽  
Large Scale ◽  
Meta Analysis ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Step Length ◽  
Free Living ◽  
Learning Methods ◽  
Machine Learning Methods

Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.

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

2019 ◽  
Vol 8 (7) ◽  
pp. 952-960 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document