Ontology Based Approach for Diagnosis in Personalized Medicine

Abstract

Due to complexity of the human body, overlapping phenotypes and complex disease networks, disease diagnosis is a real challenge for the physicians. Medical diagnostic decision support systems have been developed as an approach to ease the process of disease diagnosis. Improved patient safety, improved quality of care and improved efficiency in health care delivery are potential benefits of MDDSS.

This research proposes a novel and generic mathematical model into differential diagnosis of genetic diseases instead of traditional method of analyzing gene mutations to expose genetic diseases. It is achieved by genotype-phenotype correlation thro-ugh a common computer science concept called Ontology. Basically emerging genetic mutations of the patient are mapped to the standardized vocabulary called Human Phenotype Ontology and subsequently differential diagnosis is done using those terms. Differential diagnosis process is achieved by measuring ontology based semantic similarity by combining information theory and fuzzy relational theory. The system is capable of diagnosing the probability of occurrence of five complex diseases namely Lymphedema-Distichiasis Syndrome, Cornelia de Lange syndrome-me, Popliteal pterygium syndrome, Cohen Syndrome and Smith-Lemli-Opitz Syndrome.

We evaluate our system by comparing the results obtained from our system with domain expert’s diagnosis. Pre-diagnosed set of real Cornelia de Lange syndrome patients’ data were used in this attempt. According to the results, our system diagnoses Cornelia de Lange Syndrome with an average probability of 78.32%, Smith-Lemli-Opitz Syndrome has 61.35% probability and other three diseases with very low probability values. This thesaurus based approach which considers a correlation of phenotypes and genotype of a patient can be used by a physician to make a better diagnosis of a disease.