scholarly journals Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review (Preprint)

2020 ◽  
Author(s):  
Milena Čukić ◽  
Victoria López ◽  
Juan Pavón
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
External Validation ◽  
Clinical Diagnostics ◽  
Data Sets ◽  
Theoretical Approaches ◽  
Psychiatry Review ◽  
Machine Learning Applications ◽  
Data Driven Approach ◽  
Detection Of Depression

BACKGROUND Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. OBJECTIVE This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. METHODS To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. RESULTS We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. CONCLUSIONS This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry. CLINICALTRIAL

scholarly journals Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review

10.2196/19548 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e19548
Author(s):  
Milena Čukić ◽  
Victoria López ◽  
Juan Pavón
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
External Validation ◽  
Clinical Diagnostics ◽  
Data Sets ◽  
Theoretical Approaches ◽  
Psychiatry Review ◽  
Machine Learning Applications ◽  
Data Driven Approach ◽  
Detection Of Depression

Background Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. Objective This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. Methods To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. Results We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. Conclusions This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105213 ◽  
Author(s):  
Pradyumna Lanka ◽  
D. Rangaprakash ◽  
Sai Sheshan Roy Gotoor ◽  
Michael N. Dretsch ◽  
Jeffrey S. Katz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Resting State ◽  
Resting State Fmri ◽  
Clinical Diagnostics ◽  
Fmri Data ◽  
Matlab Toolbox

Machine Learning Applications to Resting-State Functional MR Imaging Analysis

2017 ◽  
Vol 27 (4) ◽  
pp. 609-620 ◽  
Author(s):  
John M. Billings ◽  
Maxwell Eder ◽  
William C. Flood ◽  
Devendra Singh Dhami ◽  
Sriraam Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mr Imaging ◽  
Resting State ◽  
Imaging Analysis ◽  
Machine Learning Applications ◽  
Functional Mr Imaging

scholarly journals Benchmark and Survey of Automated Machine Learning Frameworks

2021 ◽  
Vol 70 ◽  
pp. 409-472
Author(s):  
Marc-André Zöller ◽  
Marco F. Huber
Keyword(s):  
Machine Learning ◽  
Daily Life ◽  
Real Data ◽  
Data Sets ◽  
Domain Experts ◽  
Vital Part ◽  
Machine Learning Applications ◽  
Automated Machine Learning ◽  
Learning Frameworks

Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.

scholarly journals Machine Learning Econometrics: Bayesian Algorithms and Methods

2020 ◽  
Author(s):  
Dimitris Korobilis ◽  
Davide Pettenuzzo
Keyword(s):  
Machine Learning ◽  
Bayesian Inference ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Data Sets ◽  
Prior Beliefs ◽  
Machine Learning Applications ◽  
Dynamic Stochastic ◽  
Feasible Solutions ◽  
Bayesian Machine Learning

Bayesian inference in economics is primarily perceived as a methodology for cases where the data are short, that is, not informative enough in order to be able to obtain reliable econometric estimates of quantities of interest. In these cases, prior beliefs, such as the experience of the decision-maker or results from economic theory, can be explicitly incorporated to the econometric estimation problem and enhance the desired solution. In contrast, in fields such as computing science and signal processing, Bayesian inference and computation have long been used for tackling challenges associated with ultra high-dimensional data. Such fields have developed several novel Bayesian algorithms that have gradually been established in mainstream statistics, and they now have a prominent position in machine learning applications in numerous disciplines. While traditional Bayesian algorithms are powerful enough to allow for estimation of very complex problems (for instance, nonlinear dynamic stochastic general equilibrium models), they are not able to cope computationally with the demands of rapidly increasing economic data sets. Bayesian machine learning algorithms are able to provide rigorous and computationally feasible solutions to various high-dimensional econometric problems, thus supporting modern decision-making in a timely manner.

Generating Generic Data Sets for Machine Learning Applications in Building Services Using Standardized Time Series Data

2019 ◽  
Author(s):  
Florian Stinner ◽  
Yingying Yang ◽  
Thomas Schreiber ◽  
Gerrit Bode ◽  
Marc Baranski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Sets ◽  
Generic Data ◽  
Machine Learning Applications

scholarly journals A Study on machine learning methods and applications in genetics and genomics

2018 ◽  
Vol 7 (1.7) ◽  
pp. 201
Author(s):  
K Jayanthi ◽  
C Mahesh
Keyword(s):  
Machine Learning ◽  
Gene Prediction ◽  
Genomic Data ◽  
Data Sets ◽  
Complex Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Complex Data Sets ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

Machine learning enables computers to help humans in analysing knowledge from large, complex data sets. One of the complex data is genetics and genomic data which needs to analyse various set of functions automatically by the computers. Hope this machine learning methods can provide more useful for making these data for further usage like gene prediction, gene expression, gene ontology, gene finding, gene editing and etc. The purpose of this study is to explore some machine learning applications and algorithms to genetic and genomic data. At the end of this study we conclude the following topics classifications of machine learning problems: supervised, unsupervised and semi supervised, which type of method is suitable for various problems in genomics, applications of machine learning and future views of machine learning in genomics.

scholarly journals EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets

2021 ◽  
Vol 11 (6) ◽  
pp. 2823
Author(s):  
Francisco Florez-Revuelta
Keyword(s):  
Machine Learning ◽  
Image Data ◽  
Original Data ◽  
Supervised Machine Learning ◽  
Evolutionary Approach ◽  
Data Sets ◽  
Data Set ◽  
Label Data ◽  
Machine Learning Applications ◽  
Label Distribution

This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.

scholarly journals Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dekel Taliaz ◽  
Amit Spinrad ◽  
Ran Barzilay ◽  
Zohar Barnett-Itzhaki ◽  
Dana Averbuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
External Validation ◽  
Antidepressant Medication ◽  
Research Network ◽  
Test Set ◽  
Antidepressant Medications ◽  
Predictive Algorithm ◽  
Data Driven Approach ◽  
Antidepressant Use

AbstractMajor depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42–53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm’s capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm’s citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p’s < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.

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