Semi-automated Time-Granularity Detection for Data-Driven Simulation Using Process Mining and System Dynamics

2020 ◽  
pp. 77-91
Author(s):  
Mahsa Pourbafrani ◽  
Sebastiaan J. van Zelst ◽  
Wil M. P. van der Aalst
Keyword(s):  
System Dynamics ◽  
Process Mining ◽  
Data Driven

Optimal Data-Driven Modeling-Free Differential-Inversion-Based Iterative Control: A Wafer Stage Example

2020 ◽  
Author(s):  
Zezhou Zhang ◽  
Qingze Zou
Keyword(s):  
System Dynamics ◽  
High Performance ◽  
Random Noise ◽  
High Accuracy ◽  
Data Driven ◽  
Modeling Process ◽  
Random Disturbances ◽  
Noise Disturbances ◽  
Data Driven Modeling ◽  
Iterative Control

Abstract In this paper, an optimal data-driven modeling-free differential-inversion-based iterative control (OMFDIIC) method is proposed for both high performance and robustness in the presence of random disturbances. Achieving high accuracy and fast convergence is challenging as the system dynamics behaviors vary due to the external uncertainties and the system bandwidth is limited. The aim of the proposed method is to compensate for the dynamics effect without modeling process and achieve both high accuracy and robust convergence, by extending the existed modeling-free differential-inversion-based iterative control (MFDIIC) method through a frequency- and iteration-dependent gain. The convergence of the OMFDIIC method is analyzed with random noise/disturbances considered. The developed method is applied to a wafer stage, and shows a significant improvement in the performance.

Data-Driven Approaches for Developing Clinical Practice Guidelines

2017 ◽  
pp. 1307-1323
Author(s):  
Yiye Zhang ◽  
Rema Padman
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Mining ◽  
Clinical Pathways ◽  
Workflow Management ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Statistical Process

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.

Data-Driven Approaches for Developing Clinical Practice Guidelines

2017 ◽  
pp. 30-46 ◽  
Author(s):  
Yiye Zhang ◽  
Rema Padman
Keyword(s):  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Visual Analytics ◽  
Process Mining ◽  
Clinical Pathways ◽  
Workflow Management ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Statistical Process

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.

scholarly journals Data-Driven Approaches to Improve the Quality of Clinical Processes: A Systematic Review

2020 ◽  
Author(s):  
Parivash Khalili ◽  
Mohammad Reza Rasouli ◽  
Mohammad Fathian
Keyword(s):  
Process Mining ◽  
Research Question ◽  
Data Driven ◽  
Exclusion Criteria ◽  
Scientific Publications ◽  
Novel Approach ◽  
Event Based ◽  
Learning Data ◽  
Final Synthesis

Background: Considering the emergence of electronic health records and their related technologies, an increasing attention is paid to data driven approaches like machine learning, data mining, and process mining. The aim of this paper was to identify and classify these approaches to enhance the quality of clinical processes. Methods: In order to determine the knowledge related to the research question, a systematic literature review was conducted. To this end, the related studies were searched in the web of science documentation database, as a comprehensive and authoritative database covering 1536 scientific publications from 2000 to 2019. The studies found from the initial search were investigated and the relevance of their title with the inclusion and exclusion criteria was determined. As a result, 184 articles were selected. Further investigations resulted in 84 studies that remained after reviewing the abstracts and full texts of these articles. These studies were also evaluated with regard to their field of study and the quality of presented evidence. Consequently, the final synthesis was performed on the evidence extracted from these articles. Results: Examination of the identified evidences resulted in 4 general categories of "event-based approaches", "process intelligence", "clinical knowledge systems", and "data-driven control and monitoring" as data-driven approaches that can be used to manage the quality of clinical processes. Conclusion: The findings demonstrated that event-bases approaches had more applications as data driven approaches in the context of health care. Furthermore, process mining is a novel approach that can be used by future studies. The results of this study can be used to complement clinical governance procedures regarding emerging data driven opportunities.

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