Analysis of Medical Pathways by Means of Frequent Closed Sequences

Baralis, Elena; Bruno, Giulia; Chiusano, Silvia; Domenici, Virna C.; Mahoto, Naeem Ahmed; Petrigni, Caterina

doi:10.1007/978-3-642-15393-8_47

Cited by 17 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To extract performance variables to be a guidance in the process management, it is necessary to have recourse to other methods, like data mining techniques [18,19]. If time is not a monitored output, the focus is on pattern extraction in order to detect the most frequent medical treatments undergone by patients [20][21][22]. These techniques do not give a comprehensive view of the processes in act in the healthcare systems.…”

Section: State Of the Artmentioning

confidence: 99%

Application of Process Mining and Semantic Structuring Towards a Lean Healthcare Network

Antonelli

Bruno

2015

Risks and Resilience of Collaborative Networks

Self Cite

View full text Add to dashboard Cite

Modern healthcare systems are evolving towards a complex network of interconnected services. The increasing costs and the conversely increased expectations for high service levels leveraged the birth of healthcare monitoring activities and the proposition of numerous performance evaluation indicators. Generally, the adopted performance measures allow to draw a picture of quality, equity, appropriateness and efficiency of the medical care at different levels: caregiver, hospital, local health authority, region. The role of network organization and its impact on the performances is largely underestimated. It is difficult to build a Value Stream Mapping of the healthcare network because of the number and complexity of care and diseases followed. The study tries to overcome this issue. Starting from a database of the accesses to the services in a local health agency, the activity flow diagram is produced by using a process mining software, Disco. A knowledge structured by means of an ontology allows to describe the logic behind the health service provision. The resulting process flow chart is the base for the identification and amendment of redundant and non value added flows among services.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Application of Process Mining and Semantic Structuring Towards a Lean Healthcare Network

Antonelli

Bruno

2015

Risks and Resilience of Collaborative Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [16], the authors proposed a temporal pattern mining framework to find predictive patterns for detecting and diagnosing adverse medical conditions associated with a disease. Pattern extraction techniques were already proposed in [17,18,19] to detect frequent medical treatments.…”

Section: Related Workmentioning

confidence: 99%

Healthcare Network Modeling and Analysis

Antonelli¹,

Bruno²

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

View full text Add to dashboard Cite

Part 20: Health and Care NetworksInternational audienceA number of initiatives are nowadays in act to reorganize health systems and increase the interconnection of services offered to citizens to build a collaborative care network that maximizes service quality and minimizes costs. To achieve these purposes, a non standard quality assessment must be performed, as models for the analysis and evaluation of healthcare systems considered from the point of view of the entire network instead of single nodes have not been proposed yet. This paper aims at describing how to obtain a rigorous modeling and a meaningful analysis of the mass of data produced by a healthcare network. This analysis can be useful to inform the healthcare managers about the status of services under their responsibility and the degree of collaboration among entities of the network. The final outcome of these activities will be an improvement in the quality of service perceived by patients

show abstract

“…Sequential patterns have been used to identify diagnoses that diverse hospitals have in common [13] or to determine the subsets of examinations that were done together and that were followed by patients [14]. Several algorithms to discover sequential patterns have been proposed [5,15].…”

Section: Introductionmentioning

confidence: 99%

“…The concept of sequential patterns has been used extensively in different application domains [ 9 ], especially in medicine [ 10 - 12 ]. Sequential patterns have been used to identify diagnoses that diverse hospitals have in common [ 13 ] or to determine the subsets of examinations that were done together and that were followed by patients [ 14 ]. Several algorithms to discover sequential patterns have been proposed [ 5 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes

Deja

Froelich

Deja³

2015

BioMed Eng OnLine

View full text Add to dashboard Cite

BackgroundIn spite of numerous research efforts on supporting the therapy of diabetes mellitus, the subject still involves challenges and creates active interest among researchers. In this paper, a decision support tool is presented for setting insulin therapy in new-onset type 1 diabetes.MethodsThe concept of differential sequential patterns (DSPs) is introduced with the aim of representing deviations in the patient’s blood glucose level (BGL) and the amount of insulin injections administered. The decision support tool is created using data mining algorithms for discovering sequential patterns.ResultsBy using the DSPs, it is possible to support the physician’s decisionmaking concerning changing the treatment (i.e., whether to increase or decrease the insulin dosage). The other contributions of the paper are an algorithm for generating DSPs and a new method for evaluating nocturnal glycaemia. The proposed qualitative evaluation of nocturnal glycaemia improves the generalization capabilities of the DSPs.ConclusionsThe usefulness of the proposed approach was evident in the results of experiments in which juvenile diabetic patients actual data were used. It was confirmed that the proposed DSPs can be used to guide the therapy of numerous juvenile patients with type 1 diabetes.

show abstract

Analysis of Medical Pathways by Means of Frequent Closed Sequences

Cited by 17 publications

References 6 publications

Application of Process Mining and Semantic Structuring Towards a Lean Healthcare Network

Application of Process Mining and Semantic Structuring Towards a Lean Healthcare Network

Healthcare Network Modeling and Analysis

Differential sequential patterns supporting insulin therapy of new-onset type 1 diabetes

Contact Info

Product

Resources

About