scholarly journals Hospital Management Based On Semantic Process Mining: A Systematic Review

2019 ◽  
Vol 8 (1) ◽  
pp. 4 ◽  
Author(s):  
Majid Jangi ◽  
Fateme Moghbeli ◽  
Mahya Ghaffari ◽  
Alireza Vahedinemani
Keyword(s):  
Systematic Review ◽  
Business Process ◽  
Process Mining ◽  
Google Scholar ◽  
Extension Field ◽  
Exclusion Criteria ◽  
Event Logs ◽  
Business Process Mining ◽  
Improve Hospital ◽  
Semantic Process

Introduction: Semantic Process Mining is the extension field of process mining that is based on getting knowledge of conceptual event logs (based on ontologies) for analyzing frequent and rare processes. In the healthcare studies, semantic process mining has been used in different hospitals in order to improve processes.Material and Methods: A review of the usages of semantic process mining in hospitals is done. This review contains 65 articles from PubMed, dblp and Google scholar. It is searched from 2000 to 2017. One of them was duplicated and finally, we received 64 articles. Data were extracted according to PRISMA guidelines.Results: Out of 64 articles, 6 of them were related with inclusion and exclusion criteria. Most of them detect business process mining. In 80% of studies, the semantic process mining was useful and effective to improve hospital processes and improve its management.Conclusion: This review can show an overview the application of process mining in hospitals. It can help researchers to compare semantic process mining with other methods for improving processes in hospitals and finally, it shows the use of semantic process mining to enhance hospitals processes.

Generating event logs from non-process-aware systems enabling business process mining

2011 ◽  
Vol 5 (3) ◽  
pp. 301-335 ◽  
Author(s):  
Ricardo Pérez-Castillo ◽  
Barbara Weber ◽  
Jakob Pinggera ◽  
Stefan Zugal ◽  
Ignacio García-Rodríguez de Guzmán ◽  
...  
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Event Logs ◽  
Business Process Mining

Semantic Business Process Mining of SAP Transactions

2010 ◽  
pp. 416-429
Author(s):  
Jon Espen Ingvaldsen ◽  
Jon Atle Gulla
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Mining ◽  
Structured Data ◽  
The Core ◽  
Management Perspective ◽  
System Logs ◽  
Business Process Mining ◽  
Semantic Process

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

Semantic Business Process Mining of SAP Transactions

2011 ◽  
pp. 866-878 ◽  
Author(s):  
Jon Espen Ingvaldsen ◽  
Jon Atle Gulla
Keyword(s):  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Process Mining ◽  
Structured Data ◽  
The Core ◽  
Management Perspective ◽  
System Logs ◽  
Business Process Mining ◽  
Semantic Process

This chapter introduces semantic business process mining of SAP transaction logs. SAP systems are promising domains for semantic process mining as they contain transaction logs that are linked to large amounts of structured data. A challenge with process mining these transaction logs is that the core of SAP systems was not originally designed from the business process management perspective. The business process layer was added later without full rearrangement of the system. As a result, system logs produced by SAP are not process-based, but transaction-based. This means that the system does not produce traces of process instances that are needed for process mining. In this chapter, we show how data available in SAP systems can enrich process instance logs with ontologically structured concepts, and evaluate techniques for mapping executed transaction sequences with predefined process hierarchies.

Adverse Effects of Natural Products: A Brief Pre-Systematic Review

2020 ◽  
Vol 01 ◽  
Author(s):  
Carla Pires ◽  
Ana Fernandes
Keyword(s):  
Systematic Review ◽  
Adverse Event ◽  
Natural Products ◽  
Adverse Events ◽  
In Vitro Study ◽  
Google Scholar ◽  
Inclusion Criteria ◽  
Exclusion Criteria ◽  
Meta Analyses

Background: Natural products are commonly used for treating health problems. These products may be associated with adverse events, which are defined as "noxious and unintended response to a medicinal product" by the European Medicine Agency. Objectives: To identify studies describing at least one adverse event (or with potential to promote an adverse event) related to the use of natural products, as well as to describe the involved product(s) and adverse event(s). Methods: A pre-systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria. Keywords: "natural product(s)" and ["adverse drug reaction(s)" or "adverse effect(s)"]. Screened databases: PubMed, SciELO, DOAJ and Google Scholar. Inclusion criteria: papers describing at least one adverse event associated with the use of natural products and published between 2017 and 2019. Exclusion criteria: Repeated studies, reviews and papers written in other languages than English, Portuguese, French or Spanish. Results: 104 studies were identified (20 PubMed; 0 SciELO; 2 DOAJ; 82 Google Scholar), but only 10 were selected (4 PubMed and 6 Google Scholar): 1 in-vitro study; 2 non-clinical studies, 1 study reporting in-vitro and clinical data and 5 studies were cases reports. Globally, 997 reports of adverse drug reactions with natural products were identified, mainly non-severe cases. Conclusion: Since a limited number of studies was found, we conclude that adverse events due to natural products may be underreported, or natural products may have a good safety profile. This review contributes for assuring the safety of natural products consumers, by evaluating the knowledge/information on the potential adverse events and interactions of these products.

scholarly journals Business process mining

2017 ◽  
Vol 01 (01) ◽  
pp. 1630004 ◽  
Author(s):  
Asef Pourmasoumi ◽  
Ebrahim Bagheri
Keyword(s):  
Process Mining ◽  
Status Quo ◽  
Process Models ◽  
Added Value ◽  
Conformance Checking ◽  
Event Logs ◽  
Organizational Information ◽  
Organization Process ◽  
The Status ◽  
Business Process Mining

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.

scholarly journals Towards Aspects Identification in Business Process Through Process Mining

2015 ◽  
Author(s):  
Bruna Brandão ◽  
Flávia Santoro ◽  
Leonardo Azevedo
Keyword(s):  
Programming Languages ◽  
Business Process ◽  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Preliminary Evaluation ◽  
Crosscutting Concerns ◽  
Business Process Models ◽  
Event Logs ◽  
Initial Results

In business process models, elements can be scattered (repeated) within different processes, making it difficult to handle changes, analyze process for improvements, or check crosscutting impacts. These scattered elements are named as Aspects. Similar to the aspect-oriented paradigm in programming languages, in BPM, aspect handling has the goal to modularize the crosscutting concerns spread across the models. This process modularization facilitates the management of the process (reuse, maintenance and understanding). The current approaches for aspect identification are made manually; thus, resulting in the problem of subjectivity and lack of systematization. This paper proposes a method to automatically identify aspects in business process from its event logs. The method is based on mining techniques and it aims to solve the problem of the subjectivity identification made by specialists. The initial results from a preliminary evaluation showed evidences that the method identified correctly the aspects present in the process model.

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