Automated Detection of Health Changes in Patients with Systemic Lupus Erythematosus: A Probabilistic Approach

Systemic lupus erythematosus (SLE) is a lifelong disease that requires careful diagnosis, therapy, and monitoring through various medical parameters. These parameters and their interrelations serve as indicators of shifts in the disease's progression or significant health changes necessitating medical intervention. Many such relationships can be expressed using propositional logic, where the truth value of the corresponding logic formula indicates the presence of a health condition or change. Traditional binary logic is often inadequate for medical propositions that inherently require a more nuanced understanding, such as 'high sedimentation rate' or 'elevated heart rate'. This paper presents an approach leveraging probability measures to automate the detection of health changes through propositional formulas, offering a more flexible framework by incorporating fuzzy logic principles. We discuss the implications of this methodology for electronic healthcare systems and its potential impact on improving patient monitoring and care in SLE.