Abstract
The International Classification of Diseases (ICD) gives a ranking of the diagnostic codes for the classification of the diseases. The process of medical coding allocates the subset of diagnosis codes as per CMS guidelines for a patient’s visit which is critical for patient care and claims billing. Human coding intervention is error-prone, time-consuming, and expensive. This experimental study is conducted by adapting and combining the individual methods like OCR for text reading, statistical text mining, machine learning, and rule-based NLP algorithms. We tested statistical text mining and rule-based NLP approach by adapting and combining the individual techniques. Using these two distinct technology approaches applied in conjunction, developed technique has been evaluated on a set of 2500 clinical notes annotated be the human subject matter medical coding experts.
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Godbole, M., Agarwal, A. (2020). Efficacy Analysis of Technology Approaches Toward Auto-assignment of Clinical Codes to the US Patient Medical Record. In: Vasudevan, H., Michalas, A., Shekokar, N., Narvekar, M. (eds) Advanced Computing Technologies and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3242-9_40
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DOI: https://doi.org/10.1007/978-981-15-3242-9_40
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