Automated machine learning for healthcare and clinical notes analysis
A Mustafa, M Rahimi Azghadi - Computers, 2021 - mdpi.com
Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …
impact has been astonishing. To accelerate embedding ML in more applications and …
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 …
The burden and burnout in documenting patient care: an integrative literature review
The implementation of the electronic health record (EHR) across the globe has increased
significantly in the last decade. Motivations for this trend include patient safety, regulatory …
significantly in the last decade. Motivations for this trend include patient safety, regulatory …
Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention
Background This work deals with Natural Language Processing applied to Electronic Health
Records (EHRs). EHRs are coded following the International Classification of Diseases …
Records (EHRs). EHRs are coded following the International Classification of Diseases …
A unified review of deep learning for automated medical coding
Automated medical coding, an essential task for healthcare operation and delivery, makes
unstructured data manageable by predicting medical codes from clinical documents. Recent …
unstructured data manageable by predicting medical codes from clinical documents. Recent …
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] Underlying cause of death identification from death certificates using reverse coding to text and a NLP based deep learning approach
The identification of the underlying cause of death is a matter of primary importance and one
of the most challenging issues in the setting of healthcare policy making. The World Health …
of the most challenging issues in the setting of healthcare policy making. The World Health …
Multi-label classification of ICD coding using deep learning
CC Hsu, PC Chang, A Chang - 2020 International Symposium …, 2020 - ieeexplore.ieee.org
This study uses deep learning approach to tackle the multi-label classification problem in
ICD coding. The discharge summaries on MIMIC-III dataset are adopted to explore the …
ICD coding. The discharge summaries on MIMIC-III dataset are adopted to explore the …
Modelling long medical documents and code associations for explainable automatic ICD coding
W Hou, X Wang, Y Wang, J Wang, F Xiao - Expert Systems with …, 2024 - Elsevier
Abstract Quick and accurate International Classification of Diseases (ICD) code assignment
is vital for billing, reimbursement and medical research. Owing to the labour-intensive and …
is vital for billing, reimbursement and medical research. Owing to the labour-intensive and …
Do Large Language Models understand Medical Codes?
SA Lee, T Lindsey - arXiv preprint arXiv:2403.10822, 2024 - arxiv.org
The overarching goal of recent AI research has been to make steady progress towards
achieving Artificial General Intelligence (AGI), prompting the evaluation of Large Language …
achieving Artificial General Intelligence (AGI), prompting the evaluation of Large Language …