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Using text mining and machine learning for detection of child abuse

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dc.contributor.author Amrit, C., Paauw, T., Aly, R., & Lavric, M.
dc.date.accessioned 2019-04-24T17:04:44Z
dc.date.available 2019-04-24T17:04:44Z
dc.date.issued 2016
dc.identifier.citation Amrit, C., Paauw, T., Aly, R., & Lavric, M. (2016). Using text mining and machine learning for detection of child abuse. arXiv preprint arXiv:1611.03660. en_US
dc.identifier.uri https://arxiv.org/pdf/1611.03660
dc.identifier.uri http://hdl.handle.net/11212/4306
dc.description.abstract Abuse in any form is a grave threat to a child's health. Public health institutions in the Netherlands try to identify and prevent different kinds of abuse, and building a decision support system can help such institutions achieve this goal. Such decision support relies on the analysis of relevant child health data. A significant part of the medical data that the institutions have on children is unstructured, and in the form of free text notes. In this research, we employ machine learning and text mining techniques to detect patterns of possible child abuse in the data. The resulting model achieves a high score in classifying cases of possible abuse. We then describe our implementation of the decision support API at a municipality in the Netherlands. (Author Abstract) en_US
dc.language.iso en en_US
dc.publisher arXiv en_US
dc.subject child abuse en_US
dc.subject prevention en_US
dc.subject intervention en_US
dc.subject International Resources en_US
dc.subject Netherlands en_US
dc.subject internet en_US
dc.subject cybercrime en_US
dc.title Using text mining and machine learning for detection of child abuse en_US
dc.type Article en_US


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