Predicting ICD-9 Codes Using Self-Report of Patients
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-ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10046 |
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| author | Anandakumar Singaravelan Chung-Ho Hsieh Yi-Kai Liao Jia-Lien Hsu |
| author_facet | Anandakumar Singaravelan Chung-Ho Hsieh Yi-Kai Liao Jia-Lien Hsu |
| author_sort | Anandakumar Singaravelan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:06:12Z |
| format | Article |
| id | doaj.art-114cf85e20c34bc9b3a20eb0d53cab50 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:06:12Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-114cf85e20c34bc9b3a20eb0d53cab502023-11-22T20:26:59ZengMDPI AGApplied Sciences2076-34172021-10-0111211004610.3390/app112110046Predicting ICD-9 Codes Using Self-Report of PatientsAnandakumar Singaravelan0Chung-Ho Hsieh1Yi-Kai Liao2Jia-Lien Hsu3Graduate Institute of Applied Science and Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanDepartment of General Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 111045, TaiwanDepartment of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanDepartment of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 242062, TaiwanThe 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.https://www.mdpi.com/2076-3417/11/21/10046ICD-9medical recordLSTMGRU |
| spellingShingle | Anandakumar Singaravelan Chung-Ho Hsieh Yi-Kai Liao Jia-Lien Hsu Predicting ICD-9 Codes Using Self-Report of Patients Applied Sciences ICD-9 medical record LSTM GRU |
| title | Predicting ICD-9 Codes Using Self-Report of Patients |
| title_full | Predicting ICD-9 Codes Using Self-Report of Patients |
| title_fullStr | Predicting ICD-9 Codes Using Self-Report of Patients |
| title_full_unstemmed | Predicting ICD-9 Codes Using Self-Report of Patients |
| title_short | Predicting ICD-9 Codes Using Self-Report of Patients |
| title_sort | predicting icd 9 codes using self report of patients |
| topic | ICD-9 medical record LSTM GRU |
| url | https://www.mdpi.com/2076-3417/11/21/10046 |
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