Constructing model of relationship among behaviors and injuries to products based on large scale text data on injuries: Achieving evidence-based risk assessment

Koji Nomori, Koji Kitamura, Yoichi Motomura, Yoshifumi Nishida, Tatsuhiro Yamanaka, Akinori Komatsubara

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In Japan, childhood injury prevention is urgent issue. Safety measures through creating knowledge of injury data are essential for preventing childhood injuries. Especially the injury prevention approach by product modification is very important. The risk assessment is one of the most fundamental methods to design safety products. The conventional risk assessment has been carried out subjectively because product makers have poor data on injuries. This paper deals with evidence-based risk assessment, in which artificial intelligence technologies are strongly needed. This paper describes a new method of foreseeing usage of products, which is the first step of the evidence-based risk assessment, and presents a retrieval system of injury data. The system enables a product designer to foresee how children use a product and which types of injuries occur due to the product in daily environment. The developed system consists of large scale injury data, text mining technology and probabilistic modeling technology. Large scale text data on childhood injuries was collected from medical institutions by an injury surveillance system. Types of behaviors to a product were derived from the injury text data using text mining technology. The relationship among products, types of behaviors, types of injuries and characteristics of children was modeled by Bayesian Network. The fundamental functions of the developed system and examples of new findings obtained by the system are reported in this paper.

Original languageEnglish
Pages (from-to)602-612
Number of pages11
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume25
Issue number5
DOIs
Publication statusPublished - 2010 Aug 27

Keywords

  • Bayesian network
  • Childhood injury prevention
  • Knowledge creation
  • Risk assessment
  • Text mining

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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