scholarly journals Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258535
Author(s):  
Orion Weller ◽  
Luke Sagers ◽  
Carl Hanson ◽  
Michael Barnes ◽  
Quinn Snell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protective Factors ◽  
Predictive Power ◽  
Protective Factor ◽  
Peer Acceptance ◽  
Risk And Protective Factors ◽  
Learning Approaches ◽  
Suicidal Thoughts ◽  
And Behavior ◽  
Modern Machine

Introduction Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study aims to develop a prediction algorithm for STB among adolescents using the risk and protective factor framework and social determinants of health. Methods The sample population consisted of more than 179,000 high school students living in Utah and participating in the Communities That Care (CTC) Youth Survey from 2011-2017. The dataset includes responses to 300+ questions from the CTC and 8000+ demographic factors from the American Census Survey for a total of 1.2 billion values. Machine learning techniques were employed to extract the survey questions that were best able to predict answers indicative of STB, using recent work in interpretable machine learning. Results Analysis showed strong predictive power, with the ability to predict individuals with STB with 91% accuracy. After extracting the top ten questions that most affected model predictions, questions fell into four main categories: familial life, drug consumption, demographics, and peer acceptance at school. Conclusions Modern machine learning approaches provide new methods for understanding the interaction between root causes and outcomes, such as STB. The model developed in this study showed significant improvement in predictive accuracy compared to previous research. Results indicate that certain risk and protective factors, such as adolescents being threatened or harassed through digital media or bullied at school, and exposure or involvement in serious arguments and yelling at home are the leading predictors of STB and can help narrow and reaffirm priority prevention programming and areas of focused policymaking.

scholarly journals Prediction and Analysis of the Severity and Number of Suburban Accidents Using Logit Model, Factor Analysis and Machine Learning: A case study in a developing country

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Meisam Ghasedi ◽  
Maryam Sarfjoo ◽  
Iraj Bargegol
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Logit Model ◽  
Predictive Power ◽  
Dominant Role ◽  
Learning Approaches ◽  
Lighting Condition ◽  
Rainy Weather ◽  
Pedestrian Accidents ◽  
Effective Principal

AbstractThe purpose of this study is to investigate and determine the factors affecting vehicle and pedestrian accidents taking place in the busiest suburban highway of Guilan Province located in the north of Iran and provide the most accurate prediction model. Therefore, the effective principal variables and the probability of occurrence of each category of crashes are analyzed and computed utilizing the factor analysis, logit, and Machine Learning approaches simultaneously. This method not only could contribute to achieving the most comprehensive and efficient model to specify the major contributing factor, but also it can provide officials with suggestions to take effective measures with higher precision to lessen accident impacts and improve road safety. Both the factor analysis and logit model show the significant roles of exceeding lawful speed, rainy weather and driver age (30–50) variables in the severity of vehicle accidents. On the other hand, the rainy weather and lighting condition variables as the most contributing factors in pedestrian accidents severity, underline the dominant role of environmental factors in the severity of all vehicle-pedestrian accidents. Moreover, considering both utilized methods, the machine-learning model has higher predictive power in all cases, especially in pedestrian accidents, with 41.6% increase in the predictive power of fatal accidents and 12.4% in whole accidents. Thus, the Artificial Neural Network model is chosen as the superior approach in predicting the number and severity of crashes. Besides, the good performance and validation of the machine learning is proved through performance and sensitivity analysis.

PERILAKU BERESIKO DAN FAKTOR PELINDUNG PENGGUNAAN TEMBAKAU SINTETIS PADA KOMUNITAS CJ

2021 ◽  
Vol 3 (2) ◽  
pp. 78-91
Author(s):  
Yunita Anggraeni ◽  
Sitti Muthia Maghfirah Massinai ◽  
Rahma Dilla Arnanda
Keyword(s):  
Protective Factors ◽  
Drug Users ◽  
Protective Factor ◽  
Risk And Protective Factors ◽  
Community Collaboration ◽  
Community Members ◽  
The Social ◽  
The Individual ◽  
Calming Effect ◽  
The Impact

ABSTRACTSynthetic tobacco is a type of drug produced from tobacco mixed with chemical liquids. Synthetic tobacco produces a calming effect, daydreaming, hallucinations, and unconsciousness. In some individuals there is resistance to chemicals, resulting in dizziness, vomiting and unconsciousness. The basic ingredients of tobacco make synthetic tobacco difficult to distinguish from ordinary tobacco. So that the impact on the prevention and eradication of drugs is increasingly difficult. The CJ community is a community of drug users who have used synthetic tobacco for 5 months. This study aimed to determine the risk and protective factors of synthetic tobacco use in the CJ community. This research was a qualitative study on the case of the CJ community with the direct involvement of researchers in the community. The result of the research was an analysis description of the risk and protective factors in the CJ community. Medically dangerous side effects have not been communicated to users in the CJ community. Awareness of the future and responsibility are protective factors that motivate community members to stop using drugs. This research showed that the use of synthetic tobacco type drugs can be more harmful to individuals and the environment. The impact on the individual physically and psychologically will affect the individual's difficulty in interacting with the social environment. There is a need for socialization and education that takes into account various aspects of society so that prevention can take place effectively. The results of the research can become the basis for providing intervention designs with community collaboration as agents of change.Key Word: Synthetic Tobacco, Risk Factor, Protective Factor, Drugs User ABSTRAKTembakau sintetis merupakan jenis narkoba yang dihasilkan dari tembakau yang dicampur dengan cairan kimia. Tembakau sintetis menghasilkan efek tenang, melamun, halusinasi, dan tidak sadarkan diri. Pada beberapa individu terdapat penolakan terhadap zat kimia, berakibat pusing, muntah dan tidak sadarkan diri. Bahan dasar tembakau membuat tembakau sintetis sulit dibedakan dengan tembakau biasa. Sehingga berdampak kepada pencegahan dan pemberantasan narkoba yang semakin sulit. Komunitas CJ merupakan komunitas pengguna narkoba yang sudah menggunakan tembakau sintetis selama 5 bulan. Penelitian ini bertujuan untuk mengetahui risk and protective factor penggunaan tembakau sintetis pada komunitas CJ. Penelitian ini merupakan studi kualitatif pada kasus komunitas CJ dengan keterlibatan langsung peneliti pada komunitas. Hasil penelitian berupa deskripsi analisis dari risk and protective factor pada komunitas CJ. Efek samping yang berbahaya secara medis belum tersosialisasikan kepada para pengguna di komunitas CJ. Kesadaran mengenai masa depan dan tanggung jawab menjadi faktor protektif yang memotivasi anggota komunitas untuk berhenti menggunakan narkoba. Penelitian ini menunjukan bahwa penggunaan narkoba jenis tembakau sintetis dapat lebih berbahaya bagi individu dan lingkungan. Dampak kepada individu secara fisik dan psikologis akan mempengaruhi kesulitan individu berinteraksi dengan lingkungan sosial. Perlu adanya sosialisasi dan edukasi yang memperhatikan berbagai aspek di masyarakat agar pencegahan dapat berlangsung dengan efektif. Hasil penelitian dapat menjadi landasan dalam memberikan rancangan intervensi dengan kolaborasi masyarakat sebagai agen perubahan.Kata Kunci: Tembakau Sintetis, Faktor Risiko, Faktor Protektif, Pengguna Narkoba

scholarly journals Risk and protective factors for anxiety impacting academic performance in post-secondary students

2020 ◽  
Vol 50 (2) ◽  
pp. 71-88
Author(s):  
Konrad T. Lisnyj ◽  
Regan Russell ◽  
Andrew Papadopoulos
Keyword(s):  
Academic Performance ◽  
Academic Success ◽  
Protective Factors ◽  
Secondary Students ◽  
Protective Factor ◽  
Risk And Protective Factors ◽  
Survey Study ◽  
Post Secondary ◽  
Post Secondary Institution ◽  
Significant Factors

This survey study measured the association between risk and protective factors of anxiety and its implications on the academic performance of 1,053 students at a four-year, public post-secondary institution in southwestern Ontario. Logistic regression analyses revealed 13 significant variables at the univariable level, while the multivariable model yielded seven significant factors. Students who felt hopeless significantly increased their odds of reporting anxiety adversely affecting their academic performance, while being able to manage daily responsibilities was the only protective factor against anxiety impacting students’ educational attainment. By planning, designing, and implementing proactive programs focusing on thesepredictor variables, such interventions can equip students against the debilitative influence of anxiety on their academic success.

scholarly journals Machine learning for metagenomics: methods and tools

Metagenomics ◽  
2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Hayssam Soueidan ◽  
Macha Nikolski
Keyword(s):  
Machine Learning ◽  
Gene Prediction ◽  
Full Range ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Unified Framework ◽  
Comparative Metagenomics ◽  
Learning Techniques ◽  
Ngs Data ◽  
Modern Machine

AbstractOwing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.

scholarly journals RISK AND PROTECTIVE FACTORS FOR ABUSE AND NEGLECT IN DAILY CAREGIVING

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S544-S544
Author(s):  
Maria Yefimova ◽  
Carolyn Pickering ◽  
Christopher Maxwell ◽  
Frank Puga ◽  
Tami Sullivan
Keyword(s):  
Protective Factors ◽  
Process Model ◽  
Protective Factor ◽  
Elder Abuse ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Risk And Protective Factors ◽  
Care Recipient ◽  
Daily Routine ◽  
Abuse And Neglect

Abstract The stress-process model suggests a variety of factors related to the stress-experience as important in the formation of outcomes including elder abuse and neglect (EAN). Multi-level modeling with days (n=831) nested within caregivers (N=50) was used to evaluate relationships between theoretically-based risk and protective factors and odds of EAN. Disruptions in the daily routine are a significant risk factor for abuse and neglect. Participating in a meaningful activity at least twice a day with the care recipient is a significant protective factor for neglect (OR=0.19; CI=0.06-0.64; p=0.01), but not abuse. Hypotheses that spending the full day together would increase the risk of EAN, and receipt of instrumental support and caregiver participation in self-care would decrease risk, were not supported. Findings demonstrate that the risk of EAN varies from day-to-day in the presence and absence of contextual factors. Moreover, abuse and neglect may have different etiologic pathways.

scholarly journals Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach

2020 ◽  
Vol 5 (8) ◽  
pp. 62
Author(s):  
Clint Morris ◽  
Jidong J. Yang
Keyword(s):  
Machine Learning ◽  
Statistical Methods ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Crash Analysis ◽  
Suitable Alternative ◽  
Crash Data ◽  
Extreme Gradient Boosting ◽  
Modern Machine

Generating meaningful inferences from crash data is vital to improving highway safety. Classic statistical methods are fundamental to crash data analysis and often regarded for their interpretability. However, given the complexity of crash mechanisms and associated heterogeneity, classic statistical methods, which lack versatility, might not be sufficient for granular crash analysis because of the high dimensional features involved in crash-related data. In contrast, machine learning approaches, which are more flexible in structure and capable of harnessing richer data sources available today, emerges as a suitable alternative. With the aid of new methods for model interpretation, the complex machine learning models, previously considered enigmatic, can be properly interpreted. In this study, two modern machine learning techniques, Linear Discriminate Analysis and eXtreme Gradient Boosting, were explored to classify three major types of multi-vehicle crashes (i.e., rear-end, same-direction sideswipe, and angle) occurred on Interstate 285 in Georgia. The study demonstrated the utility and versatility of modern machine learning methods in the context of crash analysis, particularly in understanding the potential features underlying different crash patterns on freeways.

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