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# NLP Group Project

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Introduction

The International Classification of Diseases (ICD) standardize the format for reporting the cause of death certificate, promoting the comparability of mortality statistics internationally (CDC, 2015). To reflect the changes in the medical field, the ICD have been revised periodically and there have then ten revisions so far. In the United States, the International Classification of Diseases, Clinical Modification (ICD-9-CM), is implemented in assigning codes to diagnoses associated with inpatient, outpatient, and physician office utilization (CDC, 2015). The coding process is crucial and failure to correctly code a significant diagnosis can result in a substantial loss on reimbursement for the hospital. Given the importance in ICD coding, it is still mainly accomplished manually, which is often expensive, time-consuming, and inefficient (Li et al, 2019). Therefore, in this study, we aim to automate the ICD-9 coding by implementing a natural language processing (NLP) model on unstructured clinical notes.

Method

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Data: 
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Data are from MIMIC-III, Medical Information Mart for Intensive Care, database, compromising health information of each encounter at the critical care units of a large tertiary care hospital (Johnson et al, 2016). For this study, data of diagnostic codes and clinical notes are included.


Reference


- Centers for Disease Control and Prevention. (2015, November 6). ICD - ICD-9 - International Classification of Diseases, ninth revision. Centers for Disease Control and Prevention. Retrieved November 24, 2021, from https://www.cdc.gov/nchs/icd/icd9.htm.
- Li, M., Fei, Z., Wu, FX., Li, Y., Pan, Y., Wang, J. (2019), Automated ICD-9 Coding via A Deep Learning Approach, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 16, no. 4, pp. 1193-1202, doi: 10.1109/TCBB.2018.2817488.
- Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. W., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific data, 3, 160035. https://doi.org/10.1038/sdata.2016.35