A Study on the Repercussions of the COVID-19 Pandemic in the Mental Health of the Common Public: Machine Learning Approach

2021 ◽  
pp. 215-231
Author(s):  
Anusha Jayasimhan ◽  
Preetiha Jayashanker ◽  
S. K. Charanya ◽  
K. Krithika
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
The Common

scholarly journals Using Machine Learning To Understand Suicide: A New Approach To Classifying Australian Coroner’s Court Decisions

2021 ◽  
Author(s):  
Ravi Iyer ◽  
Elizabeth Seabrook ◽  
Suku Sukunesan ◽  
Maja Nedeljkovic ◽  
Denny Meyer
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Health Disorder ◽  
Supervised Machine Learning ◽  
Feature Engineering ◽  
Court Case ◽  
Learning Approach ◽  
Machine Learning Classification ◽  
Machine Learning Approach ◽  
Case Files

Abstract We aimed to demonstrate how a large collection of publicly accessible Australian Coroner’s Court case files (n=4459) (2009-2019) can be automatically classified for determination of death by suicide, presence of mental health disorder and sex of deceased via Natural Language Processing (NLP) methods - supervised machine learning and unsupervised dictionary-based and string search based approaches. We achieved superior levels of accuracy in the machine learning classification (Gradient Boosting vs. Random Forest baseline) of deaths by suicide of 83.3% (sensitivity = 85.1%, Specificity = 79.1%) and an accuracy of 98.3% for the dictionary-based classification of mental health disorder, as defined by the OCD-10 (sensitivity = 99.0%, specificity = 97.9%). Our machine learning approach automatically classified 24.2% (1078/4459) of the case files as referring to deaths by suicide while 63.7% (2940/4459) where classified as exhibiting a mental health disorder1. We employed a two-stage machine learning approach involving feature engineering, followed by predictive modelling in the second. Feature engineering involved several steps including removal of low value text, parts of speech analysis, term document weighting and topic clustering. Predictive classification involved extensive hyperparameter tuning to yield the most accurate model. We validated our models against a manually pre-coded subsample of case files, and also via binary logistic regression to test the contribution of each classified mental health disorder against determinations of deaths by suicide according to extant literature. This validation step confirmed elevated odds of suicide attributed to diagnoses of Depression, Schizophrenia and Obsessive Compulsive Disorder. Finally, we offer a short case study to demonstrate the efficacy of our approach in investigating a subset of case findings referring to suicides resulting from family violence. We offer a proof of concept model that demonstrates an objective and scalable approach to the analysis of legal texts. The use of NLP methods in analysing Coroner's Court case findings has important implications for the ongoing development of a real-time surveillance of suicide system in Australia.

scholarly journals Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach

2022 ◽  
Vol 12 ◽  
Author(s):  
Liana C. L. Portugal ◽  
Camila Monteiro Fabricio Gama ◽  
Raquel Menezes Gonçalves ◽  
Mauro Vitor Mendlowicz ◽  
Fátima Smith Erthal ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Social Isolation ◽  
Healthcare Workers ◽  
Intervention Programs ◽  
Depression Symptoms ◽  
Learning Approach ◽  
Professional Recognition ◽  
Machine Learning Approach

Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.

scholarly journals Mental health and resilience during the coronavirus pandemic: A machine learning approach

2021 ◽  
Author(s):  
Kristin W. Samuelson ◽  
Kelly Dixon ◽  
Joshua T. Jordan ◽  
Tyler Powers ◽  
Samantha Sonderman ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach

2020 ◽  
Vol 3 (3) ◽  
pp. 29
Author(s):  
Šimon Grác ◽  
Peter Beňo ◽  
František Duchoň ◽  
Martin Dekan ◽  
Michal Tölgyessy
Keyword(s):  
Machine Learning ◽  
Automated Detection ◽  
Learning Approach ◽  
Optimal Conditions ◽  
Detection Mechanism ◽  
Machine Learning Approach ◽  
Reliable Detection ◽  
The Common ◽  
Alternative Detection ◽  
Basic Experiments

The objective of this article is to propose and verify a reliable detection mechanism of multi-rotor unmanned aerial vehicles (UAVs). Such a task needs to be solved in many areas such as in the protection of vulnerable buildings or in the protection of privacy. Our system was firstly realized by standard computer vision methods using the Oriented FAST and Rotated BRIEF (ORB) feature detector. Due to the low success rate achieved in real-world conditions, the machine-learning approach was used as an alternative detection mechanism. The “Common Objects in Context dataset” was used as a predefined dataset and it was extended by 1000 samples of UAVs from the SafeShore dataset. The effectiveness and the reliability of our system are proven by four basic experiments—drone in a static image and videos which are displaying a drone in the sky, multiple drones in one image, and a drone with another flying object in the sky. The successful detection rate achieved was 97.3% in optimal conditions.

scholarly journals A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions

2020 ◽  
Vol 3 (7) ◽  
pp. e2010791 ◽  
Author(s):  
Isabel Chien ◽  
Angel Enrique ◽  
Jorge Palacios ◽  
Tim Regan ◽  
Dessie Keegan ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Health Interventions ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Mental Health Interventions

Source deghosting of coarsely-sampled common-receiver data using a convolutional neural network

Geophysics ◽  
2021 ◽  
pp. 1-48
Author(s):  
Jan-Willem Vrolijk ◽  
Gerrit Blacquiere
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Field Data ◽  
Simulated Data ◽  
Training Data ◽  
Learning Approach ◽  
Receiver Domain ◽  
Machine Learning Approach ◽  
The Common

It is well known that source deghosting can best be applied to common-receiver gathers, while receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, i.e., the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we propose to train a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver ghost wavefield to obtain the training data. Due to reciprocity this training data is a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach is able to remove the source-ghost wavefield from the coarsely-sampled common-receiver gathers.

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