Automated ICD coding via unsupervised knowledge integration (UNITE)
| Autor: | Chuan Hong, Yuri Ahuja, Ashwin N. Ananthakrishnan, Aaron Sonabend W, Sheng Yu, Winston Cai, Zongqi Xia |
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| Rok vydání: | 2020 |
| Předmět: |
Topic model
020205 medical informatics Computer science Medical billing Health Informatics 02 engineering and technology Machine learning computer.software_genre Article Machine Learning Automation 03 medical and health sciences Software portability 0302 clinical medicine International Classification of Diseases Knowledge integration Intensive care 0202 electrical engineering electronic engineering information engineering Electronic Health Records Humans Disease 030212 general & internal medicine Artificial neural network business.industry Unsupervised learning Neural Networks Computer Artificial intelligence business computer Algorithms Coding (social sciences) |
| Zdroj: | Int J Med Inform |
| ISSN: | 1386-5056 |
| DOI: | 10.1016/j.ijmedinf.2020.104135 |
| Popis: | Objective Accurate coding is critical for medical billing and electronic medical record (EMR)-based research. Recent research has been focused on developing supervised methods to automatically assign International Classification of Diseases (ICD) codes from clinical notes. However, supervised approaches rely on ICD code data stored in the hospital EMR system and is subject to bias rising from the practice and coding behavior. Consequently, portability of trained supervised algorithms to external EMR systems may suffer. Method We developed an unsupervised knowledge integration (UNITE) algorithm to automatically assign ICD codes for a specific disease by analyzing clinical narrative notes via semantic relevance assessment. The algorithm was validated using coded ICD data for 6 diseases from Partners HealthCare (PHS) Biobank and Medical Information Mart for Intensive Care (MIMIC-III). We compared the performance of UNITE against penalized logistic regression (LR), topic modeling, and neural network models within each EMR system. We additionally evaluated the portability of UNITE by training at PHS Biobank and validating at MIMIC-III, and vice versa. Results UNITE achieved an averaged AUC of 0.91 at PHS and 0.92 at MIMIC over 6 diseases, comparable to LR and MLP. It had substantially better performance than topic models. In regards to portability, the performance of UNITE was consistent across different EMR systems, superior to LR, topic models and neural network models. Conclusion UNITE accurately assigns ICD code in EMR without requiring human labor, and has major advantages over commonly used machine learning approaches. In addition, the UNITE attained stable performance and high portability across EMRs in different institutions. |
| Databáze: | OpenAIRE |
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