AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Clinical Coding

Showing 11 to 20 of 60 articles

Clear Filters

Automated ICD coding for primary diagnosis via clinically interpretable machine learning.

International journal of medical informatics
BACKGROUND: Computer-assisted clinical coding (CAC) based on automated coding algorithms has been expected to improve the International Classification of Disease, tenth version (ICD-10) coding quality and productivity, whereas studies oriented to pri...

Machine Learning for Medical Coding in Healthcare Surveys.

Vital and health statistics. Ser. 1, Programs and collection procedures
Objectives Medical coding, or the translation of healthcare information into numeric codes, is expensive and time intensive. This exploratory study evaluates the use of machine learning classifiers to perform automated medical coding for large statis...

A tree based approach for multi-class classification of surgical procedures using structured and unstructured data.

BMC medical informatics and decision making
BACKGROUND: In surgical department, CPT code assignment has been a complicated manual human effort, that entails significant related knowledge and experience. While there are several studies using CPTs to make predictions in surgical services, litera...

Hierarchical label-wise attention transformer model for explainable ICD coding.

Journal of biomedical informatics
International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable pred...

Multigranularity Label Prediction Model for Automatic International Classification of Diseases Coding in Clinical Text.

Journal of computational biology : a journal of computational molecular cell biology
International Classification of Diseases (ICD) serves as the foundation for generating comparable global disease statistics across regions and over time. The process of ICD coding involves assigning codes to diseases based on clinical notes, which ca...

Application of specialized word embeddings and named entity and attribute recognition to the problem of unsupervised automated clinical coding.

Computers in biology and medicine
Notes documented by clinicians, such as patient histories, hospital courses, lab reports and others are often annotated with standardized clinical codes by medical coders to facilitate a variety of secondary processing applications such as billing an...

Combining deep neural networks, a rule-based expert system and targeted manual coding for ICD-10 coding causes of death of French death certificates from 2018 to 2019.

International journal of medical informatics
OBJECTIVE: For ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team fo...

Development of a Method for Automatic Matching of Unstructured Medical Data to ICD-10 Codes.

Studies in health technology and informatics
Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complai...

EHR coding with hybrid attention and features propagation on disease knowledge graph.

Artificial intelligence in medicine
And sentences associated with these attributes and relationships have been neglected. in this paper ►We propose an end-to-end model called Knowledge Graph Enhanced neural network (KGENet) to address the above shortcomings. specifically ►We first cons...

Developing and testing a framework for coding general practitioners' free-text diagnoses in electronic medical records - a reliability study for generating training data in natural language processing.

BMC primary care
BACKGROUND: Diagnoses entered by general practitioners into electronic medical records have great potential for research and practice, but unfortunately, diagnoses are often in uncoded format, making them of little use. Natural language processing (N...