Predicting ICD-9 Codes Using Self-Report of Patients
| Autor: | Jia-Lien Hsu, Yi-Kai Liao, Anandakumar Singaravelan, Chung-Ho Hsieh |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
QH301-705.5 Computer science QC1-999 GRU Medical classification computer.software_genre medical record Component (UML) Code (cryptography) General Materials Science Segmentation Biology (General) QD1-999 Instrumentation Block (data storage) Fluid Flow and Transfer Processes Recall business.industry Physics Process Chemistry and Technology Medical record General Engineering Engineering (General). Civil engineering (General) Computer Science Applications Chemistry Identification (information) Artificial intelligence TA1-2040 business LSTM computer Natural language processing ICD-9 |
| Zdroj: | Applied Sciences Volume 11 Issue 21 Applied Sciences, Vol 11, Iss 10046, p 10046 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app112110046 |
| Popis: | The International Classification of Diseases (ICD) is a globally recognized medical classification system that aids in the identification of diseases and the regulation of health trends. The ICD framework makes it easy to keep track of records and evaluate medical data for evidence-based decision-making. Several methods have predicted ICD-9 codes based on the discharge summary, clinical notes, and nursing notes. In our study, our approach only utilizes the subjective component to predict ICD-9 codes. Data cleaning and segmentation, and Natural Language Processing (NLP) techniques are applied on the subjective component during the pre-processing. Our study builds the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) to develop a model for predicting ICD-9 codes. The ICD-9 codes contain different ICD levels such as chapter, block, three-digit code, and full code. The GRU model scores the highest recall of 57.91% in the chapter level and the top-10 experiment has a recall of 67.37%. Based on the subjective component, the model can help patients in the form of a remote assistance tool. |
| Databáze: | OpenAIRE |
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