Research Article
Child Abuse Monitor System Model: A Health Care Critical Knowledge Monitor System
@INPROCEEDINGS{10.1007/978-3-319-19656-5_37, author={Tiago Pereira and Henrique Santos}, title={Child Abuse Monitor System Model: A Health Care Critical Knowledge Monitor System}, proceedings={Internet of Things. User-Centric IoT. First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part I}, proceedings_a={IOT360}, year={2015}, month={7}, keywords={Health care knowledge sensitivity Health care decision support system Ontology Health care knowledge security Knowledge management Topic models Information retrieval Text mining}, doi={10.1007/978-3-319-19656-5_37} }- Tiago Pereira
Henrique Santos
Year: 2015
Child Abuse Monitor System Model: A Health Care Critical Knowledge Monitor System
IOT360
Springer
DOI: 10.1007/978-3-319-19656-5_37
Abstract
The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.
