Low back pain expert systems: Clinical resolution through probabilistic considerations and poset.

Objective: Proper diagnosis of Low Back Pain (LBP) is quite challenging in especially the developing countries like India. Though some developed countries prepared guidelines for evaluation of LBP with tests to detect psychological overlay, implementation of the recommendations becomes quite difficult in regular clinical practice, and different specialties of medicine offer different modes of management. Aiming at offering an expert-level diagnosis for the patients having LBP, this paper uses Artificial Intelligence (AI) to derive a clinically justified and highly sensitive LBP resolution technique.

Neural Synchronization-Guided Concatenation of Header and Secret Shares for Secure Transmission of Patients' Electronic Medical Record: Enhancing Telehealth Security for COVID-19.

This paper deals with one of the key problems of e-healthcare which is the security. Patients are worried about the confidentiality of their electronic medical record (EMR) which could be used to expose their identities. It is high time to revisit the confidentiality and security issues of the existing telehealth system.

Medical expert system for low back pain management: design issues and conflict resolution with Bayesian network.

The paper focuses on the development of a reliable medical expert system for diagnosis of low back pain (LBP) by proposing an efficient frame-based knowledge representation scheme and a suitable resolution logic with conflicts in outcomes being resolved using Bayesian network. Considering that LBP is classified into many diseases based on different pain generators, the proposed methodology infers non-conflicting LBP diseases sorted according to their chances of occurrence. A satisfactory clinical efficacy (average relative error - 0.