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Du G.,East China Normal University | Jiang Z.,Shanghai JiaoTong University | Yao Y.,Shanghai No. 6 Peoples Hospital | Diao X.,Shanghai Putuo District Central Hospital
Journal of Medical Systems | Year: 2013

In order to improve the standard of management in hospitals and effectively control the cost of clinical treatments, this research primarily focuses on optimizing the scheduling of clinical pathways (CPs). A mathematical model for CP scheduling is constructed, and the hybrid genetic algorithm (HGA, combining a genetic algorithm with particle swarm optimization) is proposed for solving this problem so as to distribute medical resources and schedule the treatments of patients reasonably and effectively. The optimal search space can be further enlarged by introducing a new mutation mechanism, which allows a more satisfactory solution to be found. In particular, the relative patient waiting time and relative time efficiency are used as measure indexes, which are more scientific and effective than the usual indexes of absolute time and absolute time efficiency. After comparing absolute waiting time, relative waiting time, utilization of absolute waiting time, and utilization of relative waiting time waiting respectively, the conclusion can confidently be drawn that task scheduling obviously enhances patients' time efficiency, reduces time wastage and therefore promotes patient satisfaction with medical processes. Moreover, the patients can to a certain degree move away from their usual passive role in medical processes by using this scheduling system. In order to further validate the rationality and validity of the proposed method, the heuristic rules for CP scheduling are also tested using the same case. The results demonstrate that the proposed HGA achieves superior performance, in terms of precision, over those heuristic rules for CP scheduling. Therefore, we utilize HGA to optimize CP scheduling, thus providing a decision-making mechanism for medical staff and enhancing the efficiency of medical processes. This research has both theoretical and practical significance for electronic CP management, in particular. © 2013 Springer Science+Business Media New York. Source


Du G.,Shanghai JiaoTong University | Jiang Z.,Shanghai JiaoTong University | Diao X.,Shanghai Putuo District Central Hospital | Yao Y.,Shanghai No. 6 Peoples Hospital
International Journal of Services Operations and Informatics | Year: 2011

Generally, there is a 'hard' problem of Clinical Pathway (CP) workflow modelling with uncertainty variances. Therefore, a CP workflow modelling method based on Modular Temporised Coloured Petri Net with changeable structure is proposed, in which the trigger mechanism of activity is introduced, and the dynamics of the treatment process along with 'time' for patients can be modelled by the transition firing and connections amongst them. With the predefined arc expressions, the automatic routes of the CP can be realised. Moreover, by using the two structural change algorithms, the CP workflow model can be dynamically updated. A case study on osteosarcoma CP workflow modelling is constructed and analysed by applying the proposed modelling method. The result shows that the built entire osteosarcoma CP model is more manageable and maintainable. Moreover, after a little modification, the model can also be applicable to other CPs workflow modelling (such as caesarean section CP). © 2011 Inderscience Enterprises Ltd. Source


Du G.,Shanghai JiaoTong University | Jiang Z.B.,Shanghai JiaoTong University | Diao X.D.,Shanghai Putuo District Central Hospital | Yao Y.,Shanghai No. 6 Peoples Hospital
International Journal of Simulation Modelling | Year: 2010

Clinical Pathway (CP) is very complicated and has many exceptional variations. Generally, its treatment course and control steps cannot be totally predefined. Meanwhile, the CP embodies the "Re-flow" therapy features, which is very hard to model, control and manage. Therefore, combined modular modelling method and structure changing mechanisms, a Modular Coloured Petri Net with changeable structure (MCPN-CS) workflow modelling method is proposed. Aimed at the variations of the CP, the workflow model for the CP can be reconfigured dynamically by using the mechanisms of change-by-modification (CBM) and change-by-composition (CBC). A case study on a workflow modelling of osteosarcoma CP is constructed and the modelling is analyzed by proposed deadlock detection algorithms (DDA). The result validates that the proposed method may noticeably enhance the flexibility, adaptation, reusability and maintainability of the workflow model for the CP. Source


Du G.,East China Normal University | Jiang Z.,Shanghai JiaoTong University | Diao X.,Shanghai Putuo District Central Hospital | Yao Y.,Shanghai No. 6 Peoples Hospital
Computers in Biology and Medicine | Year: 2013

Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. © 2013 Elsevier Ltd. Source


Du G.,Shanghai JiaoTong University | Jiang Z.,Shanghai JiaoTong University | Diao X.,Shanghai Putuo District Central Hospital | Yao Y.,Shanghai No. 6 Peoples Hospital
Journal of Medical Systems | Year: 2012

Although the clinical pathway (CP) predefines predictable standardized care process for a particular diagnosis or procedure, many variances may still unavoidably occur. Some key index parameters have strong relationship with variances handling measures of CP. In real world, these problems are highly nonlinear in nature so that it's hard to develop a comprehensive mathematic model. In this paper, a rule extraction approach based on combing hybrid genetic double multi-group cooperative particle swarm optimization algorithm (PSO) and discrete PSO algorithm (named HGDMCPSO/DPSO) is developed to discovery the previously unknown and potentially complicated nonlinear relationship between key parameters and variances handling measures of CP. Then these extracted rules can provide abnormal variances handling warning for medical professionals. Three numerical experiments on Iris of UCI data sets, Wisconsin breast cancer data sets and CP variances data sets of osteosarcoma preoperative chemotherapy are used to validate the proposed method. When compared with the previous researches, the proposed rule extraction algorithm can obtain the high prediction accuracy, less computing time, more stability and easily comprehended by users, thus it is an effective knowledge extraction tool for CP variances handling. © Springer Science+Business Media, LLC 2010. Source

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