ABSTRACT
We propose a heuristic method of using network centralities for constructing small-weight Steiner trees in this paper. The Steiner tree problem in graphs is one of the practical NP-hard combinatorial optimization problems. Given a graph and a set of vertices called terminals in the graph, the objective of the Steiner tree problem in graphs is to find a minimum weight Steiner tree that is a tree containing all the terminals. Conventional construction methods make a Steiner tree based on the shortest paths between terminals. If these shortest paths are overlapped as much as possible, we can obtain a small-weight Steiner tree. Therefore, we proposed to use network centralities to distinguish which edges should be included to make a small-weight Steiner tree. Experimental results revealed that using the vertex or the edge betweenness centralities contributes to making small-weight Steiner trees.
Subject(s)
Algorithms , Heuristics , Models, TheoreticalABSTRACT
BACKGROUND: Potentially inappropriate medications (PIMs) and polypharmacy in older adults lead to increase the risk of adverse drug events. This study aimed to evaluate the effectiveness of pharmacist intervention combining the criteria for detecting PIMs with the deprescribing algorithm on correcting PIMs, reducing the number of medications, and readmissions. METHODS: A prospective observational study was conducted at a Japanese University Hospital enrolling new inpatients aged ≥65 years prescribed ≥1 daily medication. Pharmacists detected PIMs based on the criteria combined the screening tool of older persons' potentially inappropriate prescriptions criteria version 2 with the screening tool for older persons' appropriate prescriptions for Japanese, examined changes using the deprescribing algorithm, and suggested changes to the physician. The proportion of patients whose number of medications was reduced at discharge and the rate of readmissions within 30 and 90 days were compared between patients without PIMs (without PIMs group), patients who were not suggested to change PIMs (no suggestions group), and patients who were suggested to change PIMs (suggested group). RESULTS: The study enrolled 544 patients (median age 75.0 years, 54.4% males, median number of medications 6.0/patient). The number of patients with PIMs was 240 (44.1%), and 304 patients had no PIMs (without PIMs group). Among the patients with PIMs, 125 (52.1%) patients received pharmacist suggestions to change ≥1 PIMs (suggested group), and 115 patients received no suggestions for change (no suggestions group). The total number of PIMs was 432, of which changes were suggested for 189 (43.8%). Of these 189 cases, 172 (91.0%) were changed. The proportion of patients whose number of medications was reduced was significantly higher in the suggested group than in the without PIMs group and the no suggestions group [56.8% (71/125) vs. 26.6% (81/304) and 19.1% (22/115), respectively; P < 0.001 in both comparisons]. There were no significant differences in the rates of readmissions within 30 and 90 days among the three groups. CONCLUSIONS: Pharmacist intervention combining the criteria for detecting PIMs with the deprescribing algorithm was effective for correcting PIMs and may be associated with a reduction in the number of medications.
