Integrating semantic and fuzzy dimensions into electronic medical records: Case of cerebral palsy information system
Bibliographic record
Abstract
The meta-modeling of medical records helps standardize and capitalize the expert’s knowledge domain. It promotes the interoperability knowledge and the reuse of clinical concepts, i.e., archetypes. It also promotes high quality electronic medical record system (EMRS) design, which helps provide better care service delivery. As a result, different standards of medical informatics use the dual model to support interoperability between Medical Information Systems. We particularly quote ISO/EN 13606 and OpenEHR. However, the use of these standards still presents challenges. Apart from political reasons, the main obstacles to the adoption of these standards include: (1) a lack of guides and methodological tools to facilitate the construction of EMRS using two conceptual levels. Designers must have languages, approaches and tools to assist them in the modeling of archetypal EMRS; (2) a lack of methodologies for semantic activities on the content of electronic health records in the semantic web environment; (3) and a lack of management of uncertainties and inaccuracies that may exist in the medical field. Theconstruction of an approach to modeling EMRS according to the dual model approach, considering the uncertainties, inaccuracies and semantics of these systems, is a difficult task, given the challenges to emancipate. In literature, we don’t find such an approach. We, therefore, defined one in this paper. Our goal is to guide the designer in all stages of developing a new generation of EMRS, from analysis and specification of requirements to implementation. To achieve this goal, we have created an approach to support the following activities: (1) clinical concepts and information management and meta-modeling in accordance with the openEHR standard, (2) integration of the semantic dimension into EMRS considered to enable the execution of semantic activities in the semantic web environment; and (3) integration of the fuzzy dimension into electronic medical record data structures. As a contribution, we defined an approach called Fuzzy SemanticOpenEHR allowing the integration of semantic and fuzzy dimensions into EMRS modeled using the openEHR standard. Fuzzy SemanticOpenEHR intends to help and equip the designer during the different phases of creating a fuzzy ontology. Thanks to the mechanisms offered by this approach, we have been able to obtain a fuzzy ontological basis that can serve as a knowledge base that can support the semantic interoperability between EMRS, the deduction of new knowledge and the taking of knowledge’s clinical decision. To test our contribution, we proceeded to the realization of a prototype of tools realized for the pediatric neurology service of the university hospital “Hédi Chaker Sfax - Tunisia” and the association of the handicapped persons safeguard of Sfax. This prototype is a framework called “XML 2 FuzzyOWL”. Then, we tested this framework using a case of a disease which is “Cerebral Palsy”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".