Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review

2018 ◽  
Vol 241 ◽  
pp. 519-532 ◽  
Author(s):  
Yena Lee ◽  
Renee-Marie Ragguett ◽  
Rodrigo B. Mansur ◽  
Justin J. Boutilier ◽  
Joshua D. Rosenblat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Therapeutic Outcomes ◽  
Applications Of Machine Learning

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

scholarly journals Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies

10.2196/14108 ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. e14108 ◽  
Author(s):  
Sun Jae Moon ◽  
Jinseub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Systematic Review ◽  
Magnetic Resonance ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Resonance Imaging ◽  
Accuracy Measures

Background In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. Objective This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. Methods The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). Results A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. Conclusions The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. Trial Registration PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779

scholarly journals Machine Learning Algorithms for Prediabetes Risk Calculation: a Protocol of Systematic Review and Meta-Analysis

2021 ◽  
Author(s):  
Yaltafit Abror Jeem ◽  
Refa Nabila ◽  
Dwi Ditha Emelia ◽  
Lutfan Lazuardi ◽  
Hari Kusnanto Josef
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Diagnostic Test ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment Tools ◽  
Forest Plot ◽  
Test Accuracy

Abstract Background One strategy to resolve the increasing prevalence of T2DM is to identify and administer interventions to prediabetes patients. Risk assessment tools help detect diseases, by allowing screening to the high risk group. Machine learning is also used to help diagnosis and identification of prediabetes. This review aims to determine the diagnostic test accuracy of various machine learning algorithms for calculating prediabetes risk.Methods This protocol was written in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analysis for Protocols (PRISMA-P) statement. The databases that will be used include PubMed, ProQuest and EBSCO restricted to January 1999 and May 2019 in English language only. Identification of articles will be done independently by two reviewers through the titles, the abstracts, and then the full-text-articles. Any disagreement will be resolved by consensus. The Newcastle-Ottawa Quality Assessment Scale will be used to measure the quality and potential of bias. Data extraction and content analysis will be performed systematically. Quantitative data will be visualized using a forest plot with the 95% Confidence Intervals. The diagnostic test outcome will be described by the summary receiver operating characteristic curve. Data will be analyzed using Review Manager 5.3 (RevMan 5.3) software package.Discussion We will obtain diagnostic accuracy of various machine learning algorithms for prediabetes risk estimation using this proposed systematic review and meta-analysis. Systematic review registration: This protocol has been registered in the Prospective Registry of Systematic Review (PROSPERO) database. The registration number is CRD42021251242.

scholarly journals Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Risks ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Christopher Blier-Wong ◽  
Hélène Cossette ◽  
Luc Lamontagne ◽  
Etienne Marceau
Keyword(s):  
Machine Learning ◽  
Insurance Industry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Actuarial Science ◽  
Science Field ◽  
The Past ◽  
Highly Nonlinear ◽  
Computer Scientists ◽  
Applications Of Machine Learning

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

scholarly journals Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review.

2020 ◽  
Author(s):  
Michael Moor ◽  
Bastian Rieck ◽  
Max Horn ◽  
Catherine Jutzeler ◽  
Karsten Borgwardt
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Quality Assessment ◽  
Biomarker Discovery ◽  
Learning Algorithms ◽  
Quality Criteria ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Synthesis Methods ◽  
Early Prediction

Background: Sepsis is among the leading causes of death in intensive care units (ICU) worldwide and its recognition, particularly in the early stages of the disease, remains a medical challenge. The advent of an affluence of available digital health data has created a setting in which machine learning can be used for digital biomarker discovery, with the ultimate goal to advance the early recognition of sepsis. Objective: To systematically review and evaluate studies employing machine learning for the prediction of sepsis in the ICU. Data sources: Using Embase, Google Scholar, PubMed/Medline, Scopus, and Web of Science, we systematically searched the existing literature for machine learning-driven sepsis onset prediction for patients in the ICU. Study eligibility criteria: All peer-reviewed articles using machine learning for the prediction of sepsis onset in adult ICU patients were included. Studies focusing on patient populations outside the ICU were excluded. Study appraisal and synthesis methods: A systematic review was performed according to the PRISMA guidelines. Moreover, a quality assessment of all eligible studies was performed. Results: Out of 974 identified articles, 22 and 21 met the criteria to be included in the systematic review and quality assessment, respectively. A multitude of machine learning algorithms were applied to refine the early prediction of sepsis. The quality of the studies ranged from "poor" (satisfying less than 40% of the quality criteria) to "very good" (satisfying more than 90% of the quality criteria). The majority of the studies (n= 19, 86.4%) employed an offline training scenario combined with a horizon evaluation, while two studies implemented an online scenario (n= 2,9.1%). The massive inter-study heterogeneity in terms of model development, sepsis definition, prediction time windows, and outcomes precluded a meta-analysis. Last, only 2 studies provided publicly-accessible source code and data sources fostering reproducibility. Limitations: Articles were only eligible for inclusion when employing machine learning algorithms for the prediction of sepsis onset in the ICU. This restriction led to the exclusion of studies focusing on the prediction of septic shock, sepsis-related mortality, and patient populations outside the ICU. Conclusions and key findings: A growing number of studies employs machine learning to31optimise the early prediction of sepsis through digital biomarker discovery. This review, however, highlights several shortcomings of the current approaches, including low comparability and reproducibility. Finally, we gather recommendations how these challenges can be addressed before deploying these models in prospective analyses. Systematic review registration number: CRD42020200133

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