Data Semantics Modeling on a Merise Conceptual Model for Improving Artificial Intelligence Models and Clinical Decision Making

The integration of data semantics into MERISE conceptual models offers a significant opportunity for the improvement of artificial intelligence systems applied to clinical decision making. This paper explores a systematic approach for data modeling by considering their intrinsic meaning and constraints, in order to optimize the performance of AI models and improve diagnostic accuracy.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-10
DOI
https://doi.org/10.5281/zenodo.14965857
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Data Semantics Modeling on a Merise Conceptual Model for Improving Artificial Intelligence Models and Clinical Decision Making

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

Data Semantics Modeling on a Merise Conceptual Model for Improving Artificial Intelligence Models and Clinical Decision Making

article en

Abstract

The integration of data semantics into MERISE conceptual models offers a significant opportunity for the improvement of artificial intelligence systems applied to clinical decision making. This paper explores a systematic approach for data modeling by considering their intrinsic meaning and constraints, in order to optimize the performance of AI models and improve diagnostic accuracy.

Zenodo (CERN European Organization for Nuclear Research)
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Openalex Percentile: Top 57%
Artificial Intelligence in Healthcare and Education
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Data Semantics Modeling on a Merise Conceptual Model for Improving Artificial Intelligence Models and Clinical Decision Making · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS