Automatic ICD-10 coding: Deep semantic matching based on analogical reasoning
Automatic ICD-10 coding: Deep semantic matching based on analogical reasoning (Heliyon).
Background: ICD-10 has been widely used in statistical analysis of mortality rates and medical reimbursement. Automatic ICD-10 coding is desperately needed because manually assigning codes is expensive, time-consuming, and labor-intensive. Diagnoses described in medical records differ significantly from those used in ICD-10 classification, making it impossible for existing automatic coding techniques to perform well enough to support medical billing, resource allocation, and research requirements. Meanwhile, most of the current automatic coding approaches are oriented toward English ICD-10. This method for
automatically assigning ICD-10 codes to diagnoses extracted from Chinese discharge records was provided in this paper.
[...]
results showed that the proposed method outperformed popular deep
semantic matching algorithms, such as DSSM, ConvNet, ESIM, and ABCNN, and
demonstrated state-of-the-art results in a single text matching with an
accuracy of 0.986, a precision of 0.979, a recall of 0.983, and an F1-score of
0.981.
Conclusion
The automatic ICD-10 coding of Chinese diagnoses is successful when using the
proposed deep semantic matching approach based on analogical reasoning.
[...]
Quelle: Heliyon, 19.04.2023
