We have implemented a system for assisting experts in selecting MEDLINE records for database construction purposes. This system has two specific features: The first is a learning mechanism which extracts characteristics in the abstracts of MEDLINE records of interest as patterns. These patterns reflect selection decisions by experts and are used for screening the records. The second is a keyword recommendation system which assists and supplements experts' knowledge in unexpected cases. Combined with a conventional keyword-based information retrieval system, this system may provide an efficient and comfortable environment for MEDLINE record selection by experts. Some computational experiments are provided to prove that this idea is useful.

Without intensive reading of abstracts by experts, it is almost impossible to decide if a given MEDLINE record is a target article or not. However, there may be a way to reduce the hard task of experts in selecting right articles from MEDLINE. For this purpose, we have developed an intelligent tool for selecting MEDLINE abstracts whose mechanism is based on the iterative method proposed in the paper [3]. This paper reports that, with this intelligent system, 90% of target abstracts can be selected while leaving half amount of abstracts unread with the assistance of the machine learning system BONSAI [1].