# Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?
## 4 State-of-the-Art
### Expert Systems and Decision Support Systems for NDs
One example of an ontology-based expert system for neurodegenerative diseases is the Parkinson's Disease Ontology-Driven Expert System (PD-ODES) [58]. PD-ODES is designed to assist clinicians in the diagnosis and management of PD using an ontology-based approach. The system employs a rule-based inference engine to provide recommendations for treatment and management based on patient data. PD-ODES showed promising results in preliminary studies and has the potential to improve the accuracy and consistency of diagnosis and management of PD. PD-ODES was evaluated in a pilot study with 14 patients and showed promising results in terms of the accuracy and consistency of diagnosis and management recommendations. In a study conducted by [PERSON] et al. [59], the authors explored the challenge of diagnosing AD. They suggested that a CDSS could provide a solution for this problem by offering an accurate and early diagnosis of AD. The authors noted that CDSSs can be designed using either non-knowledge-based technologies, such as machine learning techniques, or knowledge-based technologies that incorporate medical guidelines. Although they emphasized the important role of physicians, they also aimed to help health providers reach a more accurate diagnosis by eliminating the difficulties associated with diagnosingAD. However, the authors did not address how to integrate heterogeneous data types into the CDSS or how to update the system as new knowledge becomes available. [PERSON] et al. [60] developed a knowledge-based proof-of-concept (PoC) application for monitoring MS patients. The platform consists of a mobile application and a web portal that allows patients to remotely monitor their symptoms and communicate with healthcare professionals. The platform also includes CDSSs for healthcare professionals, based on clinical guidelines and patient data, to assist in the management of MS patients. The goal of the project is to improve the quality of life for MS patients, reduce the burden on healthcare systems, and increase access to specialized care for patients in remote areas. [PERSON] [61] conducted a review of existing CDSSs dedicated to the AD domain. The review was motivated by the growing interest in early detection of AD, which is key to delaying the progression of its symptoms. [PERSON] [61] found that ontology-based systems were the most used, with AlzFuzzyOnto being one of the more prominent examples. The PredictND tool, [62] which was found to increase accuracy by up to 3%, was also highlighted in the review. Other ontologies mentioned in the review include Gene-Ontology (GO) in AD, Ontology Design Patterns (ODP), Neurodegenerative Disease Data Ontology (NDDO), Alzheimer's Disease Ontology (ADO), MIND, SWAN, and SNOMED CT. These ontologies provide a semantic foundation for the diagnosis of AD and standardize the data related to this domain. Overall, the authors of both studies [59,61] highlight the potential of CDSSs to improve the accuracy of AD diagnosis and support the work of healthcare providers. The authors of the study [59,61] concluded that the use of an CDSS in combination with semantic knowledge and big data analysis holds significant potential for more accurate and differential diagnoses of NDs. This is due to the complex and heterogeneous symptoms of NDs, which can be challenging to diagnose without a comprehensive approach. One of the interesting findings in the authors' work is the EU neuGRID4 You (N4U) project [63] that was carried out in 2019. This project was based on the use of big data and fuzzy logic and aimed to calculate a patient's Alzheimer's Disease Identification Number (ADIN) using a big data repository and fuzzy processing. This calculation helped to identify patients with varying degrees of AD. The authors [64] also implemented and evaluated the Alzheimer's Disease Diagnosis Ontology (ADDO), which acts as a comprehensive semantic knowledge base. The authors [64] improved the previous version of ADDO by using fuzzy concepts and extending it into a fuzzy ontology.