scholarly journals Mental Health Quantifier

2021 ◽  
Vol 10 (5) ◽  
pp. 187-190
Author(s):  
Daksh Gupta ◽  
Aashay Markale ◽  
Rishabh Kulkarni
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Feature Extraction ◽  
Supervised Classification ◽  
Stress Disorders ◽  
Health Conditions ◽  
Classification Algorithms ◽  
Mental Health Score ◽  
Detailed Report ◽  
Definition Of

The definition of mental disorders describes them as “health conditions involving changes in emotion, thinking or behavior or a combination of these”. Contemporary societies of 2020 still fall short in recognizing some of the most common afflictions as actual problems in people. Some of those are depression, anxiety and stress disorders. This paper proposes a Machine Learning based approach wherein the analysis of the multiple-choice inputs along with a neatly curated questionnaire based on feature extraction will be done and then supervised classification algorithms will be used to generate a mental health score as well as a detailed report based on responses the user gives.

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

Digital Health Interventions for Delivery of Mental Health Care: Systematic and Comprehensive Meta-Review (Preprint)

2021 ◽  
Author(s):  
Tristan J Philippe ◽  
Naureen Sikder ◽  
Anna Jackson ◽  
Maya E Koblanski ◽  
Eric Liow ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Mobile Communications ◽  
Digital Health ◽  
Health Interventions ◽  
Stress Disorders ◽  
Health Conditions ◽  
Rapid Review ◽  
Mental Health Conditions

BACKGROUND The COVID-19 pandemic has shifted mental health care delivery to digital platforms, video conferencing, and other mobile communications. However, existing reviews of digital health interventions are narrow in scope and focus on a limited number of mental health conditions. OBJECTIVE To address this gap, we conducted a rapid review of the literature to assess the state of digital health interventions for the treatment of several mental health conditions. METHODS We searched MEDLINE for secondary literature published between 2010-2021 on the use, efficacy, and appropriateness of digital health interventions for the delivery of mental health care. RESULTS Sixty percent (60%) of research involved the treatment of substance use disorders, 25% focused on mood, anxiety and traumatic stress disorders and 5% or less on other mental health conditions. Synchronous and asynchronous communication, computerized therapy, and cognitive training appear to be effective, but require further examination in understudied mental health conditions. Similarly, virtual reality, mobile apps, social media platforms, and online forums are novel technologies that have the potential to improve mental health but require higher quality evidence. CONCLUSIONS Digital health interventions offer promise in the treatment of mental health conditions. In the context of the COVID-19 pandemic, digital health interventions provide a safer alternative to face-to-face treatment. However, further research on the applications of digital interventions in understudied mental health conditions is needed. Additionally, evidence is needed on the effectiveness and appropriateness of digital health tools for patients, who are marginalized, and may lack access to digital health interventions.

scholarly journals Police Officers’ Ability in Recognizing Relevant Mental Health Conditions

2021 ◽  
Vol 12 ◽  
Author(s):  
Linus Wittmann ◽  
Gunter Groen ◽  
Petra Hampel ◽  
Ronja Petersen ◽  
Astrid Jörns-Presentati
Keyword(s):  
Mental Health ◽  
Police Officers ◽  
Stress Disorders ◽  
Health Conditions ◽  
Post Traumatic Stress ◽  
Perceived Knowledge ◽  
Actual Knowledge ◽  
Mental Health Conditions ◽  
Traumatic Stress Disorders ◽  
Post Traumatic

The recognition of certain mental health conditions is important as this requires police officers to communicate and behave in an adjusted manner with affected individuals. The objective of the present study was to test police officers’ knowledge about mental health symptoms as a component of their mental health literacy (MHL) and to examine if police officers’ perceived knowledge corresponds with their actual knowledge. A questionnaire was used to assess for MHL representing mental health conditions which occur frequently in police requests (schizophrenia, bipolar disorder, depression, post-traumatic stress disorders, and emotionally unstable personality disorder). Furthermore, the questionnaire assessed the frequency of police requests, the officers’ perceived knowledge regarding mental disorders and their sense of feeling sufficiently trained to deal with these kinds of requests. Eighty-two police officers participated in the study. Police officers’ actual knowledge about mental health conditions did not correspond with their perceived knowledge. Participants revealed a moderately high level of overall knowledge which differed with regard to symptoms of each of the five mental health conditions. The mental status of a paranoid schizophrenia was best identified by the police officers and the majority correctly allocated the symptoms. Post-traumatic stress disorders and manic episodes were only identified by a minority of police offers. Police training geared to prepare for requests involving individuals with mental disorders should expand this limited knowledge transfer and focus on a broader variety of mental health conditions that police officers frequently encounter in requests.

scholarly journals Machine learning in mental health: a scoping review of methods and applications

2019 ◽  
Vol 49 (09) ◽  
pp. 1426-1448 ◽  
Author(s):  
Adrian B. R. Shatte ◽  
Delyse M. Hutchinson ◽  
Samantha J. Teague
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Scoping Review ◽  
Latent Dirichlet Allocation ◽  
Health Conditions ◽  
Support Vector ◽  
Mental Health Conditions ◽  
Big Data Applications ◽  
Study Results ◽  
Detection And Diagnosis

AbstractBackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

scholarly journals Machine learning in mental health: A systematic scoping review of methods and applications

2018 ◽  
Author(s):  
Adrian Shatte ◽  
Delyse Hutchinson ◽  
Samantha Teague
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Big Data ◽  
Psychological Functioning ◽  
Latent Dirichlet Allocation ◽  
Health Conditions ◽  
Support Vector ◽  
Mental Health Conditions ◽  
Study Results ◽  
Detection And Diagnosis

Objective This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. Materials and MethodsEight health and information technology research databases were searched using the terms “big data” or “machine learning” and “mental health”. Articles were assessed by two reviewers, and data were extracted on the article’s mental health application, ML technique, data type and size, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health; and, (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer’s Disease. ML techniques used included support vector machines, decision trees, neural networks, latent dirichlet allocation, and clustering.Discussion and ConclusionOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to improve other areas of psychological functioning. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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