نهمین کنگره نقشه برداری مغز

نمایش مقاله

Title:

Development of semantic framework and ontology for neurodegenerative diseases and investigating the role of machine learning and deep learning approaches in clinical outcomes.

Subject:

Artificial Intelligence and Brain Mapping

Presenter Author:
Name:	Sharare Taheri Moghadam

Author:
Name 1:	Sharare Taheri Moghadam - Corresponding - Presenter
Affiliation 1:	School of Health Management and Information Sciences, Iran University of Medical Sciences Tehran, Iran

Name 2:
Affiliation 2:

Name 3:
Affiliation 3:

Name 4:
Affiliation 4:

Name 5:
Affiliation 5:

Name 6:
Affiliation 6:

Name 7:
Affiliation 7:

Name 8:
Affiliation 8:

Name 9:
Affiliation 9:

Name 10:
Affiliation 10:

Abstract

Original Research

introduction :

Neurodegenerative diseases (NDs) represent a diverse group of progressive neurological disorders characterized by complex clinical manifestations and underlying pathological mechanisms. Among these, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most prevalent and impose a significant global health burden. Early detection, accurate diagnosis, and effective disease management remain challenging due to disease heterogeneity, overlapping clinical features, and the integration of diverse biomedical data sources. Recent advances in artificial intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have demonstrated considerable potential for analyzing complex neurodegenerative disease data. The purpose of this study is to develop a semantic framework and ontology for neurodegenerative diseases and to investigate the role of ML and DL approaches in improving the understanding and clinical outcomes of these diseases.

Methods:

This multidisciplinary research was conducted in two main stages. In the first stage, relevant data and domain knowledge related to neurodegenerative diseases were collected from clinical, biological, and cognitive sources to design and develop a semantic framework and ontology. Ontology engineering principles were applied to formally represent disease concepts, relationships, and hierarchical structures in a machine-readable and semantically consistent manner. In the second stage, existing applications of ML and DL techniques in neurodegenerative disease data analysis were systematically reviewed. This stage focused on examining how AI-based methods have been used for disease prediction, classification, progression analysis, and outcome assessment, as well as their interaction with structured and unstructured biomedical data.

Results:

The study resulted in the development of a comprehensive semantic framework and ontology that organizes key concepts related to neurodegenerative diseases, including disease subtypes, clinical features, biomarkers, and disease stages. The findings indicate that semantic frameworks and ontologies enhance the organization, integration, and retrieval of domain knowledge, leading to improved accuracy and recall in knowledge-based analyses. The review of AI applications demonstrates that ML and DL approaches are effective in identifying complex patterns within neurodegenerative disease data and show strong potential for early prediction and diagnosis when applied to well-structured datasets. The combination of semantic knowledge representation with AI techniques supports more consistent and meaningful analysis of heterogeneous neurodegenerative disease data.

Conclusion:

This study highlights the importance of semantic frameworks and ontologies as foundational tools for organizing neurodegenerative disease knowledge and enabling advanced data analysis. By providing a structured representation of disease-related information, the proposed semantic framework facilitates the effective application of ML and DL methods in neurodegenerative disease research. The integration of ontology-based knowledge representation with AI approaches contributes to improved understanding, prediction, and management of neurodegenerative diseases. The outcomes of this research support the development of ontology-based analytical tools and architectures that can enhance future studies and applications in the field of neurodegenerative disease analysis.

Keywords:

Ontology, Artificial intelligence, Deep learning, Neurodegenerative Disease, Alzheimer, Semantic framework