Name
Abstract | ||
|---|---|---|
Abuse in any form is a grave threat to a childu0027s 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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computers and Society | Public health,Data science,Data mining,Text mining,Computer science,Decision support system,Knowledge management,Cybercrime,Artificial intelligence,Machine learning,The Internet |
DocType | Volume | Citations |
Journal | abs/1611.03660 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chintan amrit | 1 | 160 | 19.11 |
| Tim Paauw | 2 | 0 | 0.34 |
| Robin Aly | 3 | 263 | 26.80 |
| Miha Lavric | 4 | 0 | 0.34 |