Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning

Perron, Brian E.; Victor, Bryan G.; Bushman, Gregory; Moore, Andrew; Ryan, Joseph P.; Lu, Alex Jiahong; Piellusch, Emily K.

doi:10.1016/j.chiabu.2019.104180

Cited by 19 publications

(12 citation statements)

References 17 publications

(18 reference statements)

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“…In terms of the implementation, four articles ( n = 4) were included [ 26 , 28 , 33 , 37 ], and three issues were identified: end users’ acceptance ( n = 1) [ 26 ], professional compliance ( n = 2) [ 28 , 33 ], and professional training ( n = 1) [ 37 ]. As discussed by Amrit et al (2017) [ 26 ], end users (i.e., nurses and physicians) have limited understanding of the model development or how to apply a prototype (i.e., early product sample) in their work; providing further information in these regards is important to increase the acceptance by end users.…”

Section: Resultsmentioning

confidence: 99%

Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review

Hui

Constantino

Lee

2023

IJERPH

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Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.

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Section: Resultsmentioning

confidence: 99%

Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review

Hui

Constantino

Lee

2023

IJERPH

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“…또한, 유아기에는 개인이 목표를 설 정하고, 설정한 목표를 달성하기 위해 사고, 정서, 행동을 의 식적으로 관리하고 조절하는 고차원적 인지 기능인 실행기능 이 발달한다 (Carlson & Moses, 2001;Luciana, 2013;Zelazo & Müller, 2002) (Luthar, 1991;Yates, Egeland, & Sroufe, 2003), 탄력성이 란 스트레스가 증가하는 상황일지라도 영향을 덜 받거나 거의 받지 않으며, 그 상황에서 대처하는 데에 유능감을 발휘하여 스트레스를 현저히 낮출 수 있는 능력 (Luthar, 1991) (Lantz, 2013). 머신러닝 기법은 다양한 연구 분야 에 적용되고 있는데, 특히 영유아와 관련하여서는 아동학대나 아동복지정책을 위한 연구(S. H. Lee, Maenner, & Heilig, 2019;Perron et al, 2019;Taylor et al, 2019), 발달지연이나 장애예측 연구 (Ambili & Afsar, 2016;Goodfellow et al, 2018)…”

Section: 사회적 유능성은 유아의 적응력과 자아정체감 형성에 영향unclassified

Developing Prediction Model for Children’s Social Competence Using Machine Learning

Lee¹

2022

Korean J Child Stud

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Objectives: This study aims to identify the types of latent classes of children’s social competence, and to develop a model using machine learning to predict the type and identify relatively important variables.Methods: Data were collected from 466 children aged three to five years and their mothers. Children’s social competence was classified by level. Latent class analysis, machine learning model construction, and performance evaluation were performed using R 3.6.1 and R-Studio 1.2.5033. The machine learning algorithms used were logistic regression, lasso logistic regression, random forest, and gradient-boosted decision tree models.Results: First, according to the characteristics of the latent class of children’s social competence, it was classified into two types: ‘high level’ and ‘low level’. Second, a machine learning algorithm was applied according to the latent class. The best performing model was the random forest model. Third, the most important variable in predicting the social competence type was identified as ‘harm avoidance’ in the children’s temperament. Fourth, another major variable was a ‘shift’ in the children’s executive functions.Conclusion: This study is meaningful as it suggests the possibility of predicting and discriminating children’s social competence and various developmental aspects by applying machine learning, the latest technique, to predict the types of children’s social competence.

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“…In this phase, teacher's or student's requests are semantically preprocessed using text mining methods. For automatic text mining, we used R libraries using RStudio for semantic personalized preferences extraction [ 11 , 29 , 32 , 41 ]. Text mining is a type of data mining used to understand and find hidden relations in natural language text semantically [ 11 , 29 , 32 , 41 ].…”

Section: Proposed Systemmentioning

confidence: 99%

“…For automatic text mining, we used R libraries using RStudio for semantic personalized preferences extraction [ 11 , 29 , 32 , 41 ]. Text mining is a type of data mining used to understand and find hidden relations in natural language text semantically [ 11 , 29 , 32 , 41 ]. After the preprocessing, the data is stored in a repository.…”

Section: Proposed Systemmentioning

confidence: 99%

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

Ali

Hafeez

Abbas

et al. 2021

Multimed Tools Appl

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The education system worldwide has been affected by the Corona Virus Diseases 2019 (COVID-19) pandemic, resulting in the interruption of all educational institutions. Moreover, as a precautionary measure, the lockdown has been imposed that has severely affected the learning processes, especially assessment activities, including exams and viva. In such challenging situations, E-learning platforms could play a vital role in conducting seamless academic activities. In spite of all the advantages of remote learning systems, many hurdles and obstacles, like a selection of suitable learning resources/material encounter by individual users based on their interests or requirements. Especially those who are not well familiar with the internet technology in developing countries and are in need of a platform that could help them in resolving the issues related to the online virtual environment. Therefore, in this work, we have proposed a mechanism that intelligently and correctly predicts the appropriate preferences for the selection of resources relevant to a specific user by considering the capabilities of diverse perspectives users to provide quality online education and to make work from home policy more effective and progressive during the pandemic. The proposed system helps teachers in providing quality online education, familiarizing them with advanced technology in the online environment. It also semantically predicts the preferences for virtual assistance of those users who are in need of learning the new tools and technologies in short time as per their institutional requirements in order to meet the quality standards of online education. The experimental and statistical results have demonstrated that the proposed virtual personalized preferences system has improved overall academic activities as compared to the current method. The proposed system enhanced user's learning abilities and facilitated them in selecting short courses while using different online education tools adopted/suggested by the institutions to conduct online classes/seminars/webinars etc., as compared to the conventional classes/activities.

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Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning

Abstract: Detecting substance-related problems in narrative investigation summaries of child abuse and neglect using text mining and machine learning. Child Abuse & Neglect,

Cited by 19 publications

References 17 publications

Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review

Harnessing Machine Learning in Tackling Domestic Violence—An Integrative Review

Developing Prediction Model for Children’s Social Competence Using Machine Learning

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic

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