scholarly journals Rise of the machines? Machine learning approaches and mental health: opportunities and challenges

2018 ◽  
Vol 213 (3) ◽  
pp. 509-510 ◽  
Author(s):  
Paul A. Tiffin ◽  
Lewis W. Paton
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Statistical Modelling ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Potential Benefits

SummaryMachine learning methods are being increasingly applied to physical healthcare. In this article we describe some of the potential benefits, challenges and limitations of this approach in a mental health context. We provide a number of examples where machine learning could add value beyond conventional statistical modelling.Declaration of interestNone.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

Machine Learning Approaches for Sentiment Analysis

2014 ◽  
pp. 193-208 ◽  
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

Machine Learning Approaches for Sentiment Analysis

Big Data ◽  
2016 ◽  
pp. 1917-1933
Author(s):  
Basant Agarwal ◽  
Namita Mittal
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Knowledge Based ◽  
Semantic Orientation

Opinion Mining or Sentiment Analysis is the study that analyzes people's opinions or sentiments from the text towards entities such as products and services. It has always been important to know what other people think. With the rapid growth of availability and popularity of online review sites, blogs', forums', and social networking sites' necessity of analysing and understanding these reviews has arisen. The main approaches for sentiment analysis can be categorized into semantic orientation-based approaches, knowledge-based, and machine-learning algorithms. This chapter surveys the machine learning approaches applied to sentiment analysis-based applications. The main emphasis of this chapter is to discuss the research involved in applying machine learning methods mostly for sentiment classification at document level. Machine learning-based approaches work in the following phases, which are discussed in detail in this chapter for sentiment classification: (1) feature extraction, (2) feature weighting schemes, (3) feature selection, and (4) machine-learning methods. This chapter also discusses the standard free benchmark datasets and evaluation methods for sentiment analysis. The authors conclude the chapter with a comparative study of some state-of-the-art methods for sentiment analysis and some possible future research directions in opinion mining and sentiment analysis.

scholarly journals Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial

2020 ◽  
Vol 30 (Suppl 1) ◽  
pp. 217-228 ◽  
Author(s):  
Sanjay Basu ◽  
James H. Faghmous ◽  
Patrick Doupe
Keyword(s):  
Machine Learning ◽  
Health Disparities ◽  
Precision Medicine ◽  
Learning Approaches ◽  
Learning Methods ◽  
Medicine Research ◽  
Machine Learning Methods ◽  
Box Models ◽  
Precision Medicine Research ◽  
Key Terms

  Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health research­ers on the application of machine learning methods to conduct precision medicine research designed to reduce health dispari­ties. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advan­tages and disadvantages of different learning approaches, describe strategies for interpret­ing “black box” models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.Ethn Dis. 2020;30(Suppl 1):217-228; doi:10.18865/ed.30.S1.217

scholarly journals Review of machine learning methods in soft robotics

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246102
Author(s):  
Daekyum Kim ◽  
Sang-Hun Kim ◽  
Taekyoung Kim ◽  
Brian Byunghyun Kang ◽  
Minhyuk Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soft Materials ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Methods ◽  
Soft Robots ◽  
Machine Learning Methods ◽  
Wearable Robots ◽  
Soft Actuators

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots.

scholarly journals Modelling MTPL insurance claim events: Can machine learning methods overperform the traditional GLM approach?

2021 ◽  
Vol 4 (2) ◽  
pp. 34-69
Author(s):  
Dávid Burka ◽  
László Kovács ◽  
László Szepesváry
Keyword(s):  
Machine Learning ◽  
Network Models ◽  
Statistical Modelling ◽  
Third Party ◽  
Insurance Companies ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Long Time ◽  
Generalised Linear Modelling

Pricing an insurance product covering motor third-party liability is a major challenge for actuaries. Comprehensive statistical modelling and modern computational power are necessary to solve this problem. The generalised linear and additive modelling approaches have been widely used by insurance companies for a long time. Modelling with modern machine learning methods has recently started, but applying them properly with relevant features is a great issue for pricing experts. This study analyses the claim-causing probability by fitting generalised linear modelling, generalised additive modelling, random forest, and neural network models. Several evaluation measures are used to compare these techniques. The best model is a mixture of the base methods. The authors’ hypothesis about the existence of significant interactions between feature variables is proved by the models. A simplified classification and visualisation is performed on the final model, which can support tariff applications later.

scholarly journals Advanced machine learning methods in psychiatry: an introduction

2020 ◽  
Vol 33 (2) ◽  
pp. e100197 ◽  
Author(s):  
Tsung-Chin Wu ◽  
Zhirou Zhou ◽  
Hongyue Wang ◽  
Bokai Wang ◽  
Tuo Lin ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Artificial Neural

Mental health questions can be tackled through machine learning (ML) techniques. Apart from the two ML methods we introduced in our previous paper, we discuss two more advanced ML approaches in this paper: support vector machines and artificial neural networks. To illustrate how these ML methods have been employed in mental health, recent research applications in psychiatry were reported.

scholarly journals Machine learning to predict early recurrence after oesophageal cancer surgery

2019 ◽  
Author(s):  
Saqib A Rahman ◽  
Robert C Walker ◽  
Megan A Lloyd ◽  
Ben L Grace ◽  
Gijs I van Boxel ◽  
...  
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Neoadjuvant Treatment ◽  
Early Recurrence ◽  
Machine Learning Techniques ◽  
Oesophageal Adenocarcinoma ◽  
Ensemble Model ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods

ABSTRACTObjectiveTo develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multi-national cohort.Summary Background DataEarly cancer recurrence after oesophagectomy is a common problem with an incidence of 20-30% despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. Machine learning techniques potentially allow more accurate prognostication and have been applied in this study.MethodsConsecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in 6 UK and 1 Dutch oesophago-gastric units were analysed. Using clinical characteristics and post-operative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and XG boost (XGB). Finally, a combined (Ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model.ResultsIn total 812 patients were included. The recurrence rate at less than 1 year was 29.1%. All of the models demonstrated good discrimination. Internally validated AUCs were similar, with the Ensemble model performing best (ELR=0.785, RF=0.789, XGB=0.794, Ensemble=0.806). Performance was similar when using internal-external validation (validation across sites, Ensemble AUC=0.804). In the final model the most important variables were number of positive lymph nodes (25.7%) and vascular invasion (16.9%).ConclusionsThe derived model using machine learning approaches and an international dataset provided excellent performance in quantifying the risk of early recurrence after surgery and will be useful in prognostication for clinicians and patients.DRAFT VISUAL ABSTRACTIcons taken from www.flaticon.com, made by ‘Freepik’, ‘smashicons’, and ‘prettycons’. Reproduced under creative commons attribution licenseMINI-ABSTRACTEarly recurrence after surgery for adenocarcinoma of the oesophagus is common. We derived a risk prediction model using modern machine learning methods that accurately predicts risk of early recurrence using post-operative pathology

scholarly journals EXPLAIN IT TO ME – FACING REMOTE SENSING CHALLENGES IN THE BIO- AND GEOSCIENCES WITH EXPLAINABLE MACHINE LEARNING

2020 ◽  
Vol V-3-2020 ◽  
pp. 817-824
Author(s):  
R. Roscher ◽  
B. Bohn ◽  
M. F. Duarte ◽  
J. Garcke
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Neural Networks ◽  
Simulated Data ◽  
High Accuracy ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Recent Advances ◽  
Post Hoc

Abstract. For some time now, machine learning methods have been indispensable in many application areas. Especially with the recent development of efficient neural networks, these methods are increasingly used in the sciences to obtain scientific outcomes from observational or simulated data. Besides a high accuracy, a desired goal is to learn explainable models. In order to reach this goal and obtain explanation, knowledge from the respective domain is necessary, which can be integrated into the model or applied post-hoc. We discuss explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results. We show that recent advances in machine learning to enhance transparency, interpretability, and explainability are helpful in overcoming these challenges.

scholarly journals A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

2019 ◽  
Author(s):  
Leila Mirsadeghi ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Seyed Reza Beh-Afarin ◽  
Reza Haji Hosseini ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Ensemble Methods ◽  
Feature Space ◽  
Ensemble Classifier ◽  
Cancer Prognosis ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

Abstract Background: Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance.Methods: This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study is to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies as regards EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures.Results: By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values.Conclusions: To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

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