IKAR: An Interdisciplinary Knowledge-Based Automatic Retrieval Method from Chinese Electronic Medical Record

To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in...

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Bibliographic Details
Published in:Information (Basel) 2023-01, Vol.14 (1), p.49
Main Authors: Zhao, Yueming, Hu, Liang, Chi, Ling
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Datasets
decision support model
Deep learning
Efficiency
Electronic health records
Fetuses
Gynecology
Hospitals
Information retrieval
Interdisciplinary aspects
Machine learning
Medical records
Methods
Natural language processing
Neural networks
Obstetrics
obstetrics and gynecology
Ultrasonic imaging
ultrasound report
Umbilical cord
Uterus
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Summary:To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in obstetrics and gynecology was established and made publicly available. Based on the ultrasound reports in the dataset, a new information retrieval method (IKAR) is proposed to extract key information from the ultrasound reports and automatically generate the corresponding ultrasound diagnostic results. The model can both extract what is already in the report and analyze what is not in the report by inference. After applying the IKAR method to the dataset, it is proved that the method could achieve 89.38% accuracy, 91.09% recall, and 90.23% F-score. Moreover, the method achieves an F-score of over 90% on 50% of the 10 components of the report. This study provides a quality dataset for the field of electronic medical records and offers a reference for information retrieval methods in the field of obstetrics and gynecology or in other fields.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14010049