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

• 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
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14010049

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.