Suggestive Diagnostic Process in a Case of Multiple Myeloma with Gastrointestinal Immunoglobulin Light-Chain Amyloidosis Accompanied by Protein-Losing Enteropathy

Multiple myeloma is a type of plasma cell neoplasm that produces monoclonal immunoglobulin. Multiple myeloma is known to cause immunoglobulin light-chain (AL) amyloidosis, which frequently involves the kidney and heart. Bone pain or fractures caused by osteolytic lesions and physical disorders related to renal or cardiac AL amyloidosis are major initial symptoms in multiple myeloma. Multiple myeloma diagnosed from the gastrointestinal symptoms is rare. We report a case of an 80-year-old man with multiple myeloma accompanied by gastrointestinal AL amyloidosis and secondary protein-losing enteropathy. The diagnostic process was suggestive, in that diarrhea and refractory leg edema related to protein-losing enteropathy were the primary symptoms and the trigger for making a sequential diagnosis of gastrointestinal AL amyloidosis and underlying multiple myeloma. This case is highly suggestive, in that multiple myeloma with gastrointestinal AL amyloidosis should be considered one of the background diseases of protein-losing enteropathy.

Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

In this work we present strategies for (optimal) measurement computation and selection in model- based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and guarantee- ing query properties existing methods do not provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems.

On Active Learning Strategies for Sequential Diagnosis

When diagnosing a faulty system one is often confronted with a large number of possible fault hypotheses. Sequential Diagnosis (SD) techniques aim at the localization or identification of the ac- tual fault with minimal cost or effort. SD can be viewed as an Active Learning (AL) task where the learner, trying to find some target hypothesis, formulates sequential queries to some oracle, thereby e.g. requesting additional system measurements. Several query selection measures (QSMs) for de- termining the best query to ask next have been proposed for AL. To date, few of them have been translated to and employed in SD. In this work, we account for this and analyze various QSMs wrt. to the discrimination power of their selected queries within the diagnostic hypotheses space. As a result, we derive superiority and equivalence relations between these QSMs and introduce improved versions of existing QSMs to overcome identified issues. The obtained picture gives a hint about which QSMs should preferably be used in SD to choose a query from a pool of candidates. Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on these, we devise an efficient heuristic search for optimal queries. As (preliminary) evaluation results indicate, the latter is especially beneficial in applications where query generation is costly, e.g. involving logical reasoning, and hence a pool of query candidates is not (cheaply) available.