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Xiaodi Diao

Bio: Xiaodi Diao is an academic researcher. The author has contributed to research in topics: Workflow & Petri net. The author has an hindex of 8, co-authored 12 publications receiving 193 citations.

Papers
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Journal ArticleDOI
Yan Ye1, Zhibin Jiang1, Xiaodi Diao, Dong Yang1, Gang Du1 
TL;DR: An ontology-based approach of modeling clinical pathway workflows at the semantic level for facilitating computerized clinical pathway implementation and efficient delivery of high-quality healthcare services is proposed.

73 citations

Journal ArticleDOI
TL;DR: A weighted fuzzy reasoning algorithm is designed to address the reasoning problem of uncertain goal propositions and known goal concepts by combining forward reasoning with backward reasoning and therefore to facilitate cause analysis and handling of workflow exceptions.
Abstract: Exception handling plays a key role in dynamic workflow management that enables streamlined business processes. Handling application-specific exceptions is a knowledge-intensive process involving different decision-making strategies and a variety of knowledge, especially much fuzzy knowledge. Current efforts in workflow exception management are not adequate to support the knowledge-based exception handling. This paper proposes a hybrid exception handling approach based on two extended knowledge models, i.e., generalized fuzzy event-condition-action (GFECA) rule and typed fuzzy Petri net extended by process knowledge (TFPN-PK). The approach realizes integrated representation and reasoning of fuzzy and non-fuzzy knowledge as well as specific application domain knowledge and workflow process knowledge. In addition, it supports two handling strategies, i.e., direct decision and analysis-based decision, during exception management. The approach fills in the gaps in existing related researches, i.e., only providing the capability of direct exception handling and neglecting fuzzy knowledge. Based on TFPN-PK, a weighted fuzzy reasoning algorithm is designed to address the reasoning problem of uncertain goal propositions and known goal concepts by combining forward reasoning with backward reasoning and therefore to facilitate cause analysis and handling of workflow exceptions. A prototype system is developed to implement the proposed approach.

27 citations

Journal ArticleDOI
TL;DR: The hybrid genetic algorithm (HGA, combining a genetic algorithm with particle swarm optimization) is proposed for solving the scheduling of clinical pathways so as to distribute medical resources and schedule the treatments of patients reasonably and effectively.
Abstract: 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.

25 citations

Journal ArticleDOI
TL;DR: 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), developed to discovery the previously unknown and potentially complicated nonlinear relationship between key parameters and variances handling measures of CP.
Abstract: 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.

18 citations

Journal ArticleDOI
Gang Du1, Zhibin Jiang1, Xiaodi Diao, Yan Ye1, Yang Yao 
TL;DR: A new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm, and achieves superior performances in efficiency, precision, and generalization ability.
Abstract: Clinical pathways' variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients' life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs.

17 citations


Cited by
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Journal ArticleDOI
TL;DR: This work presents an overview of the improved FPN theories and models from the perspectives of reasoning algorithms, knowledge representations and FPN models, and offers directions for future research to improve the FPN performance.

151 citations

Journal ArticleDOI
TL;DR: The experimental results indicate the applicability of the proposed approach, based on which it is possible to discover clinical pathway patterns that can cover most frequent medical behaviors that are most regularly encountered in clinical practice, holds significant promise in research efforts related to the analysis of clinical pathways.

133 citations

Journal ArticleDOI
15 Jun 2018-Energy
TL;DR: The robustness evaluation, which is based on untrained driving cycles test, measurement noise test and piecewise training and batteries test, indicates the good performance on estimation accuracy, applicability and robustness of the proposed methods.

117 citations

Journal ArticleDOI
TL;DR: A knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems of domain experts' diversity experience and reason the rule-based knowledge more intelligently is presented.
Abstract: The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

112 citations