Abstract
In the medical and insurance industry, electronic medical record materials contain a lot of information, which can be extracted and applied to various businesses through artificial intelligence technology, which will greatly reduce labor costs and improve efficiency. However, it is difficult to extract. At present, most of them rely on manual input. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP) technology to electronize and structure the information on these paper materials has gradually become a hot spot in the current industry. Based on this, we constructed a medical material information extraction data set Medical OCR dataset (MedOCR) [1], and we also held the “Medical inventory invoice OCR element extraction Task” evaluation competition based on the eighth China Health Information processing Conference (CHIP2022), in order to promote the development of medical material information extraction technology. A total of 18 teams participated in the competition, most of which used an OCR-based extraction system. For the evaluation index Acc, the best performing teams reached 0.9330 and 0.9076. The task of the competition focuses on information extraction technology, and MedOCR will be open for researchers to carry out related technical research for a long time.
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Liu, L., Chang, D., Zhao, X., Guo, L., Chen, M., Tang, B. (2023). Information Extraction of Medical Materials: An Overview of the Track of Medical Materials MedOCR. In: Tang, B., et al. Health Information Processing. Evaluation Track Papers. CHIP 2022. Communications in Computer and Information Science, vol 1773. Springer, Singapore. https://doi.org/10.1007/978-981-99-4826-0_13
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DOI: https://doi.org/10.1007/978-981-99-4826-0_13
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