.png){kind=icon}
返回
Automated ICD coding via unsupervised knowledge integration (UNITE)
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijmedinf.2020.104135.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Automated ICD assignment
Electronic medical records
Portability
Semantic embedding
Knowledge integration
Unsupervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
4.6K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notesOAAI
OAAI分享收藏
查看更多内容