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Accurate assessment of disease progression requires proper understanding of natural disease process which is often hidden and unobservable. For this purpose, disease status should be clearly detected. But in most diseases it is not possible to detect such status. This study, therefore, aims to present a model which both investigates the unobservable disease process and considers the error probability in diagnosis of disease states.

Assessing Misdiagnosis of Relapse in Patients with Gastric Cancer in Iran Cancer Institute Based on a Hidden Markov Multi-state Model