Stroke Medical Ontology for Supporting AI-based Stroke Prediction System using Bio-Signals

In this paper, we propose a stroke medical ontology that provides medical knowledge to accompany AI-based stroke disease prediction system's results that were arrived at based on EMG information. This system was developed as a result of the limitations mentioned above being encountered in previous studies. We approached the problem from a viewpoint of knowledge engineering with the aim of modeling medical knowledge related to strokes. Using web ontology language (OWL), a standard ontology language, we developed schema-level stroke ontologies with concepts and properties based on the brain's anatomical structures, lesions, and disease related to strokes. Also, we developed an instance-level medical terms ontology that can span standard medical terms such as those in the international classification diseases (ICD), systematized nomenclature of medicine - clinical terms (SNOMED-CT), and foundational model of anatomy (FMA). The above schema ontology and instance ontology are meaningfully mapped to each other to apply layered ontology modeling techniques that separate schemas from instances. Through semantic web rule language (SWRL)-based inference, we predict lesions, diseases, and anatomical brain structural ripple effects based on the patient's current lesions and diseases. The inferred knowledge information is provided via the SPARQL protocol and RDF query language (SPARQL), a standard ontology query language. To verify the stroke medical ontology proposed in this paper, we developed an ontology-based stroke disease prediction system. This system achieved knowledge augmentation performance of 67.82% by comparing the patients' current lesions and diseases with the lesions, diseases, and areas of disability found by SWRL-based inference using actual stroke emergency data from 37 patients.

I. Introduction

Stroke is a disease that occurs in the brain and can affect all body organs [1]. Strokes are the result of either cerebral infarction or cerebral hemorrhage [1]. More than 66% of stroke patients die immediately after the stroke or suffer from severe disabilities as a result [2]. One precursor symptom before a stroke occurs is transient ischemic attack (TIA), is also known as a mini-stroke [3]. A mini-stroke is an event that is recovered from within 24 hours of what is a temporary cerebrovascular blockage occurring [3]. Mini-strokes are recovered from immediately after experiencing symptoms, thus are often dismissed without recognition. However, 5-10% of patients who have experienced a mini-stroke develop a cerebral infarction later in life [3]. The best method for preventing a stroke is to predict a mini-stroke situation in a timely manner and provide an emergency alarm to the user so they can receive the appropriate medical treatment by being taken to a hospital.