# Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?
## 4 State-of-the-Art
### Expert Systems and Decision Support Systems for NDs  
[PERSON] et al. presented a new multi-scale ontology-based modeling technology for the accurate and progressive diagnosis of PD in their study [50]. The authors aimed to exploit heterogeneous patient data and match new associated physical and biological biomarkers through a multi-layer neural network. The input for their expert system was the patient's health record (PHR), including various data such as neuropsychological test scores, brain tissue samples, genetic material and mutations, molecular data, MRIs, electrophysiological data, EEGs, biomarkers detected from blood, and other measurement data of mitochondrial dysfunctions. The knowledge base used by the expert system was an ontology-basedmodel that combined structured vocabularies with a small set of relationships between the members, which described gene product characteristics, gene product annotation, and diseases. This vocabulary was specified with definitions of classes, relations, functions, and other objects. The authors' ultimate goal was to identify the patient's status and appropriate treatment, which was made possible through the use of a multi-layer neural network. Although the results of the proposed model were promising, the authors acknowledged that the validation of their model was a crucial aspect that needed to be addressed in future research. [PERSON] et al. [51] developed a clinical decision support system (CDSS) for the diagnosis of MS to help physicians diagnose MS with a relapsing-remitting phenotype. The CDSS was developed in four stages: requirement analysis, system design, system development, and system evaluation. To evaluate the efficiency and applicability of the software, the data of 130 medical records of patients aged 20 to 40 between 2017 and 2019 were used. The results of the evaluations of the efficiency and applicability of the software demonstrate the high acceptability of the system and the satisfaction of all of the operational expectations of users. The CDSS provided guidance in decision-making and helped physicians in the timely and accurate diagnosis of MS.  
[PERSON] et al. tackled the challenge of differentiating and accurately diagnosing AD [52; 53] by proposing a fuzzy ontology called AlzFuzzyOnto [54]. This fuzzy ontology is related to AD concepts and enables the semantic representation of medical data for the diagnosis and continuous support of AD patients. The ontology incorporated the uncertainties and inaccuracies associated with AD through the concept of fuzziness. The authors used the mind ontology [55] as an initial ontology and extended it to develop AlzFuzzyOnto. They also utilized SNOMED (Systematized Nomenclature of Medicine-Clinical Terms) CT [56], a structured clinical vocabulary for use in an electronic health record, and the Unified Medical Language System (UMLS) Metathesaurus [57] as standards for enabling the sharing and reuse of this domain ontology. UMLS is a large biomedical thesaurus that is organized by concept or meaning that links synonymous names from over 200 different source vocabularies. The initial classes used were \"Doctor\", \"Patient\", \"Diagnosis\", \"Enrolment\", \"Follow-Up\", \"Test\", and \"TestValue\". The authors added further classes related to AD diagnosis, questioning, symptoms, and entourage as well as classes representing general concepts regarding AD support such as \"Therapies\", \"Phase\", and \"Current State\". They fuzzified various classes such as \"Patient\", \"Pharmacotherapy\", \"Non-Pharmacotherapy\", \"Current State\", and \"Diagnosis\", and introduced the fuzzy relation \"In\". All of the classes were related to other classes through properties such as HasSymptoms, HasTherapies, HasCurrentState, Corresponding Patient, Corresponding Follow-Up, HasDiagnosis, and HasEnrollment. Although the proposed ontology was innovative and showed potential, the authors noted that it still needed to be validated in the future.