Harmonization of ICF Body Structures and ICD-11 Anatomic Detail: one foundation for two classifications
Della Mea, V.; Almborg, A.-H.; MArtinuzzi, M.; Tu, S. W.; Martinuzzi, A.
Show abstract
The Family of International Classifications of the World Health Organization currently includes three reference classifications, namely International Classification of Diseases (ICD), International Classification of Functioning, Disability, and Health (ICF), and International Classification of Health Interventions (ICHI). Each of them serves a specific classification need. However, they share some common concepts that are present, in different forms, in two or all of them. One important set of shared concepts is the representation of human anatomy entities, which are not always modeled in the same way and with the same level of detail. To understand the relationships among the three anatomical representations, an effort is needed to compare them, identifying common areas, gaps, and compatible and incompatible modeling. The work presented here contributes to this effort, focusing on the anatomy representations in ICF and ICD-11. For this aim, three experts were asked to identify, for each entity in the ICF Body Structures, one or more entities in the ICD-11 Anatomic Detail that could be considered identical, broader or narrower. To do this, they used a specifically developed web application, which also automatically identified the most obvious equivalences. A total of 631 maps were independently identified by the three mappers for 218 ICF Body Structures, with an interobserver agreement of 93.5%. Together with 113 maps identified by the software, they were then consolidated into 434 relations. The results highlight some differences between the two classifications: in general, ICF is less detailed than ICD-11; ICF favors lumping of structures; in very few cases, the two classifications follow different anatomic models. For these issues, solutions have to be found that are compliant with the WHO approach to classification modeling and maintenance.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- GenECG: A synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development 92%
- Network Graph Representation of COVID-19 Scientific Publications to Aid Knowledge Discovery 92%
- Development of a customised data management system for a COVID-19-adapted colorectal cancer pathway 91%
Similar papers in this journal
- A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data 92%
- Digitizing ECG image: new fully automated method and open-source software code 91%
- Automated IntraVascular UltraSound Image Processing and Quantification of Coronary Artery Anomalies: The AIVUS-CAA software 90%
Similar papers in this journal
- FATAL: A Forensic AuTopsy Annotation tooL for digital recording of autopsy findings 92%
- Fusion of Electronic Health Records and Radiographic Images for a Multimodal Deep Learning Prediction Model of Atypical Femur Fractures 92%
- Deep learning ensemble for abdominal aortic calcification scoring from lumbar spine X-ray and DXA images 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.