Argumentation Analytics for Treatment Deliberations in Multimorbidity Cases: An Introduction to Two Artificial Intelligence Approaches

Multimorbidity, the presence of multiple health conditions that must be addressed, is a particularly difficult situation in patient management raising issues such as the use of multiple drugs and drug-disease interactions. Clinical Guidelines are evidence-based statements which provide recommendations for specific health conditions but are unfit for the management of multiple co-occurring health situations. To leverage these evidence-based documents, it becomes necessary to combine them. In this paper, using a case example, we explore the use of argumentation schemes to reason and combine evidence-based recommendations from clinical guidelines, expected effects, conflicts stemming from said recommendations, and preferences regarding treatment goals. We compare the results of reasoning using the schemes for practical reasoning and argument from negative consequences in the Carneades Argumentation System with those of ASPIC-G, an extension of the artificial intelligence system ASPIC+.

     

Reduced delayed-rectifier K+ current in the learning mutant rutabaga

In the Drosophila mutant rutabaga, short-term memory is deficient and intracellular cyclic adenosine monophosphate (cAMP) concentration is reduced. We characterized the delayed-rectifier potassium current (IK_DR) in rutabaga as compared with the wild-type. The conventional whole-cell patch-clamp technique was applied to cultured Drosophila neurons derived from embryonic neuroblasts. IK_DR was smaller in rutabaga (368±11 pA) than in wild-type (541±14 pA) neurons, measured in a Ca²⁺-free solution. IK_DR was clearly activated at ~0 mV in the two genotypes. IK_DR typically reached its peak within 10–20 msec after the start of the pulse (60 mV). There was no difference in inactivation of IK_DR for wild-type (14±3%) and rutabaga (19±3%). After application of 10 mM TEA, in wild-type, IK_DR was reduced by 46±5%, whereas in rutabaga, IK_DR was reduced by 28±3%. Our results suggest that IK_DR is carried by two different types of channels, one which is TEA-sensitive, whereas the other is TEA-insensitive. Apparently, the TEA-sensitive channel is less expressed in rutabaga neurons than in wild-type neurons. Conceivably, altered neuronal excitability in the rutabaga mutant could disrupt the processing of neural signals necessary for learning and memory.