A visual approach to enhance coordination among diagnostic units using self-organizing map
Jha, R.K.,
A visual approach to enhance coordination among diagnostic units using self-organizing map - 27-42 p.
The paper presents a modified visual clustering method for patients visiting diagnostic units (DUs) using self-organizing map approach. The clustering of patients in homogenous groups helps healthcare managers in efficient scheduling of patients in each homogenous group such that their waiting time can be minimized. The grouping of patients would also help in enhancing coordination among diagnostic units (DUs). Modified Kohonen’s self-organizing map (SOM) was used to solve the visual clustering problem. Two distinct cases for patients visiting diagnostic units of clustering problems were solved in this paper. In the first case, patients are allowed to visit DU’s in any order of sequence. In the second case, patients are allowed to visit DUs based on a predefined sequence. Numerical experiments were conducted using randomly generated data sets. Finally, performance of modified visual SOM approach was measured using grouping efficiency for the first case and group technology efficiency for the second case.
Self-organizing map Visual clustering Patient-diagnostic cluster unit Grouping efficiency Group technology efficiency
A visual approach to enhance coordination among diagnostic units using self-organizing map - 27-42 p.
The paper presents a modified visual clustering method for patients visiting diagnostic units (DUs) using self-organizing map approach. The clustering of patients in homogenous groups helps healthcare managers in efficient scheduling of patients in each homogenous group such that their waiting time can be minimized. The grouping of patients would also help in enhancing coordination among diagnostic units (DUs). Modified Kohonen’s self-organizing map (SOM) was used to solve the visual clustering problem. Two distinct cases for patients visiting diagnostic units of clustering problems were solved in this paper. In the first case, patients are allowed to visit DU’s in any order of sequence. In the second case, patients are allowed to visit DUs based on a predefined sequence. Numerical experiments were conducted using randomly generated data sets. Finally, performance of modified visual SOM approach was measured using grouping efficiency for the first case and group technology efficiency for the second case.
Self-organizing map Visual clustering Patient-diagnostic cluster unit Grouping efficiency Group technology efficiency