Publication: Collecting Child Psychiatry Documents of Clinical Trials from PubMed by the SVM Text Classification Method with the MATF Weighting Scheme
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2020-01-01
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21945365
21945357
21945357
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2-s2.0-85065916379
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Mahidol University
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SCOPUS
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Advances in Intelligent Systems and Computing. Vol.936, (2020), 99-108
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Jantima Polpinij, Tontrakant Kachai, Kanyarat Nasomboon, Poramin Bheganan Collecting Child Psychiatry Documents of Clinical Trials from PubMed by the SVM Text Classification Method with the MATF Weighting Scheme. Advances in Intelligent Systems and Computing. Vol.936, (2020), 99-108. doi:10.1007/978-3-030-19861-9_10 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/49587
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Collecting Child Psychiatry Documents of Clinical Trials from PubMed by the SVM Text Classification Method with the MATF Weighting Scheme
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© 2020, Springer Nature Switzerland AG. Child psychiatry is a branch of psychiatry focused on the diagnosis, treatment, and prevention of mental health issues in children and their families. In many countries, the study of disorders such as ADHD (Attention-Deficit/Hyperactivity Disorder) by child and adolescent psychiatry is still in its infancy, with the result that children’s mental health issues can be the source of embarrassment for the family and of shame for many children. Misunderstanding, denying, and ignoring children’s mental health issues by parents are the main problem encountered in diagnosis and treatment of mental health issues in children. To help parents and extended families understand this problem better, and thus help them to better care for children with mental health issues, starting with seeking help from a psychiatrist without embarrassment, an easily accessible and reliable source of information is urgently needed. To develop such a single source of information, relevant documents need to be gathered together. This study presents a method of gathering reports of clinical trials from PubMed which describe diagnosis and treatment of child mental health issues. The main mechanism of the proposed method is a Support Vector Machine with a Multi Aspect TF (MATF) weighting scheme. After testing by recall, precision, and F1, it can return satisfactory results of 0.82, 0.79, and 0.80 respectively.