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1. Machine learning and big data analytics in bipolar disorder : A position paper from the International Society for Bipolar Disorders Big Data Task Force

Author

Passos, Ives C., Ballester, Pedro L., Barros, Rodrigo C., Librenza-Garcia, Diego, Mwangi, Benson, Birmaher, Boris, Brietzke, Elisa, Hajek, Tomas, Lopez Jaramillo, Carlos, Mansur, Rodrigo B., Alda, Martin, Haarman, Bartholomeus C. M., Isometsa, Erkki, Lam, Raymond W., McIntyre, Roger S., Minuzzi, Luciano, Kessing, Lars V., Yatham, Lakshmi N., Duffy, Anne, Kapczinski, Flavio, Department of Psychiatry, HUS Psychiatry, and University of Helsinki

Subjects
bipolar disorder, PREDICTING SUICIDALITY, RISK, MOOD DISORDERS, SYMPTOMS, predictive psychiatry, education, 3112 Neurosciences, deep learning, data mining, ASSOCIATION, personalized psychiatry, DEPRESSION, CLASSIFICATION, 3124 Neurology and psychiatry, risk prediction, machine learning, big data, LITHIUM RESPONSE, SCHIZOPHRENIA, NEUROPROGRESSION
Abstract

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Published
2019

2. Machine learning and big data analytics in bipolar disorder

Author

Luciano Minuzzi, Erkki Isometsä, Elisa Brietzke, Diego Librenza-Garcia, Anne Duffy, Martin Alda, Benson Mwangi, Flávio Kapczinski, Rodrigo B. Mansur, Boris Birmaher, Bartholomeus C M Haarman, Roger S. McIntyre, Lars Vedel Kessing, Raymond W. Lam, Lakshmi N. Yatham, Pedro Ballester, Tomas Hajek, Ives Cavalcante Passos, Carlos López Jaramillo, and Rodrigo C. Barros

Subjects
SYMPTOMS, Computer science, Big data, Scientific literature, computer.software_genre, Field (computer science), Terminology, risk prediction, 0302 clinical medicine, big data, SCHIZOPHRENIA, NEUROPROGRESSION, bipolar disorder, RISK, ASSOCIATION, Prognosis, DEPRESSION, 3. Good health, Psychiatry and Mental health, Phenotype, machine learning, LITHIUM RESPONSE, MOOD DISORDERS, Schizophrenia (object-oriented programming), Advisory Committees, Clinical Decision-Making, education, Machine learning, Risk Assessment, CLASSIFICATION, Suicidal Ideation, 03 medical and health sciences, medicine, Humans, Bipolar disorder, Biological Psychiatry, PREDICTING SUICIDALITY, business.industry, Deep learning, predictive psychiatry, Data Science, deep learning, data mining, medicine.disease, personalized psychiatry, 030227 psychiatry, Position paper, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Published
2019

3. A survey of big data classification strategies.

Author

Banchhor, Chitrakant and Srinivasu, N.

Subjects
BIG data, MACHINE learning, ARTIFICIAL intelligence, DEEP learning, DATA mining
Abstract

Big data plays nowadays a major role in finance, industry, medicine, and various other fields. In this survey, 50 research papers are reviewed regarding different big data classification techniques presented and/or used in the respective studies. The classification techniques are categorized into machine learning, evolutionary intelligence, fuzzy-based approaches, deep learning and so on. The research gaps and the challenges of the big data classification, faced by the existing techniques are also listed and described, which should help the researchers in enhancing the effectiveness of their future works. The research papers are analyzed for different techniques with respect to software tools, datasets used, publication year, classification techniques, and the performance metrics. It can be concluded from the here presented survey that the most frequently used big data classification methods are based on the machine learning techniques and the apparently most commonly used dataset for big data classification is the UCI repository dataset. The most frequently used performance metrics are accuracy and execution time. [ABSTRACT FROM AUTHOR]

Published
2020

4. WOA + BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network.

Author

Hassib, Eslam. M., El-Desouky, Ali. I., Labib, Labib. M., and El-kenawy, El-Sayed M.

Subjects
MULTILAYER perceptrons, PARTICLE swarm optimization, RECURRENT neural networks, MACHINE learning, FEATURE selection, WHALES, BIG data, REGRESSION trees
Abstract

Nowadays, big data plays a substantial part in information knowledge analysis, manipulation, and forecasting. Analyzing and extracting knowledge from such big datasets are a very challenging task due to the imbalance of data distribution, which could lead to a biased classification results and wrong decisions. The standard classifiers are not capable of handling such datasets. Hence, a new technique for dealing with such datasets is required. This paper proposes a novel classification framework for big data that consists of three developed phases. The first phase is the feature selection phase, which uses the Whale optimization algorithm (WOA) for finding the best set of features. The second phase is the preprocessing phase, which uses the SMOTE algorithm and the LSH-SMOTE algorithm for solving the class imbalance problem. Lastly, the third phase is WOA + BRNN algorithm, which is using the Whale optimization algorithm for training a deep learning approach called bidirectional recurrent neural network for the first time. Our proposed algorithm WOA-BRNN has been tested against nine highly imbalanced datasets one of them is big dataset in terms of area under curve (AUC) against four of the most common use machine learning algorithms (Naïve Bayes, AdaBoostM1, decision table, random tree), in addition to GWO-MLP (training multilayer perceptron using Gray Wolf Optimizer), then we test our algorithm over four well-known datasets against GWO-MLP and particle swarm optimization (PSO-MLP), genetic algorithm (GA-MLP), ant colony optimization (ACO-MLP), evolution strategy (ES-MLP), and population-based incremental learning (PBIL-MLP) in terms of classification accuracy. Experimental results proved that our proposed algorithm WOA + BRNN has achieved promising accuracy and high local optima avoidance, and outperformed four of the most common use machine learning algorithms, and GWO-MLP in terms of AUC. [ABSTRACT FROM AUTHOR]

Published
2020
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