A Novel Grammar-Based Approach for Patients’ Symptom and Disease Diagnosis Information Dissemination to Maintain Confidentiality and Information Integrity

Disease prediction using computer-based methods is now an established area of research. The importance of technological intervention is necessary for the better management of disease, as well as to optimize use of limited resources. Various AI-based methods for disease prediction have been documented in the literature. Validated AI-based systems support diagnoses and decision making by doctors/medical practitioners. The resource-efficient dissemination of the symptoms identified and the diagnoses undertaken is the requirement of the present-day scenario to support paperless, yet seamless, information sharing. The representation of symptoms using grammar provides a novel way for the resource-efficient encoding of disease diagnoses. Initially, symptoms are represented as strings, and, in terms of grammar, this is called a sentence. Moreover, the conversion of the generated string containing the symptoms and the diagnostic outcome to a QR code post encryption makes it portable. The code can be stored in a mobile application, in a secure manner, and can be scanned wherever required, universally. The patient can carry the medical condition and the diagnosis in the form of the QR code for medical consultations. This research work presents a case study based on two diseases, influenza and coronavirus, to highlight the proposed methodology. Both diseases have some common and overlapping symptoms. The proposed system can be implemented for any kind of disease detection, including clinical and diagnostic imaging.