The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks.
Beinlich IA, Suermondt HJ, Chavez RM, Cooper GF. The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In: Proceedings of the Conference on Artificial Intelligence in Medical Care, 1989.
ALARM (A Logical Alarm Reduction Mechanism) is a diagnostic application used to explore probabilistic reasoning techniques in belief networks. ALARM implements an alarm message system for patient monitoring; it calculates probabilities for a differential diagnosis based on available evidence. The medical knowledge is encoded in a graphical structure connecting 8 diagnoses, 16 findings and 13 intermediate variables. Two algorithms were applied to this belief network: (1) a message-passing algorithm by Pearl for probability updating in multiply connected networks using the method of conditioning; and (2) the Lauritzen-Spiegelhalter algorithm for local probability computations on graphical structures. The characteristics of both algorithms are analyzed and their specific applications and time complexities are shown.