Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies
MG Kersloot, FJP van Putten, A Abu-Hanna… - Journal of biomedical …, 2020 - Springer
Background Free-text descriptions in electronic health records (EHRs) can be of interest for
clinical research and care optimization. However, free text cannot be readily interpreted by a …
clinical research and care optimization. However, free text cannot be readily interpreted by a …
[HTML][HTML] Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review
Abstract Background Natural Language Processing (NLP) applications have developed
over the past years in various fields including its application to clinical free text for named …
over the past years in various fields including its application to clinical free text for named …
A label attention model for ICD coding from clinical text
ICD coding is a process of assigning the International Classification of Disease diagnosis
codes to clinical/medical notes documented by health professionals (eg clinicians). This …
codes to clinical/medical notes documented by health professionals (eg clinicians). This …
[HTML][HTML] Hierarchical label-wise attention transformer model for explainable ICD coding
Abstract International Classification of Diseases (ICD) coding plays an important role in
systematically classifying morbidity and mortality data. In this study, we propose a …
systematically classifying morbidity and mortality data. In this study, we propose a …
[HTML][HTML] “Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks
One unintended consequence of the Electronic Health Records (EHR) implementation is the
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …
[HTML][HTML] Use of the systematized nomenclature of medicine clinical terms (SNOMED CT) for processing free text in health care: systematic scoping review
Background: Interoperability and secondary use of data is a challenge in health care.
Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized …
Specifically, the reuse of clinical free text remains an unresolved problem. The Systematized …
[HTML][HTML] Systematized nomenclature of medicine–clinical terminology (SNOMED CT) clinical use cases in the context of electronic health record systems: systematic …
R Vuokko, A Vakkuri, S Palojoki - JMIR medical informatics, 2023 - medinform.jmir.org
Background The Systematized Medical Nomenclature for Medicine–Clinical Terminology
(SNOMED CT) is a clinical terminology system that provides a standardized and …
(SNOMED CT) is a clinical terminology system that provides a standardized and …
[HTML][HTML] Automated ICD coding for primary diagnosis via clinically interpretable machine learning
X Diao, Y Huo, S Zhao, J Yuan, M Cui, Y Wang… - International journal of …, 2021 - Elsevier
Background Computer-assisted clinical coding (CAC) based on automated coding
algorithms has been expected to improve the International Classification of Disease, tenth …
algorithms has been expected to improve the International Classification of Disease, tenth …
Construction of a semi-automatic ICD-10 coding system
L Zhou, C Cheng, D Ou, H Huang - BMC medical informatics and decision …, 2020 - Springer
Abstract Background The International Classification of Diseases, 10th Revision (ICD-10)
has been widely used to describe the diagnosis information of patients. Automatic ICD-10 …
has been widely used to describe the diagnosis information of patients. Automatic ICD-10 …
[HTML][HTML] Retrieve and rerank for automated ICD coding via Contrastive Learning
Automated ICD coding is a multi-label prediction task aiming at assigning patient diagnoses
with the most relevant subsets of disease codes. In the deep learning regime, recent works …
with the most relevant subsets of disease codes. In the deep learning regime, recent works …