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.14965856
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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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)
Machine Learning in Healthcare
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)
Peace, Justice and strong institutions
Openalex Percentile: Top 75%
Machine Learning in Healthcare
DOIOpen Access PDFOpenAlex
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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