Process mining framework with time perspective for understanding acute care: a case study of AIS in hospitals

Abstract Background Acute care for critical illness requires very strict treatment timeliness. However, healthcare providers usually cannot accurately figure out the causes of low efficiency in acute care process due to the lack of effective tools. Besides, it is difficult to compare or conformance processes from different patient groups. Methods To solve these problems, we proposed a novel process mining framework with time perspective, which integrates four steps: standard activity construction, data extraction and filtering, iterative model discovery, and performance analysis. Results It can visualize the execution of actual clinical activities hierarchically, evaluate the timeliness and identify bottlenecks in the treatment process. We take the acute ischemic stroke as a case study, and retrospectively reviewed 420 patients’ data from a large hospital. Then we discovered process models with timelines, and identified the main reasons for in-hospital delay. Conclusions Experiment results demonstrate that the framework proposed could be a new way of drawing insights about hospitals’ clinical process, to help clinical institutions increase work efficiency and improve medical service.

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Discovering Business Processes from Email Logs using fastText and Process Mining

10.36227/techrxiv.12283835 ◽

2020 ◽

Author(s):

Yaghoub rashnavadi ◽

Sina Behzadifard ◽

Reza Farzadnia ◽

sina zamani

Keyword(s):

Machine Learning ◽

Business Processes ◽

Oil And Gas ◽

Process Mining ◽

The Body ◽

Process Models ◽

Supervised Machine Learning ◽

Implicit Information ◽

Oil And Gas Sector

<p>Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.</p>

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Process Modelling Based on Event Logs

Multidisciplinary Aspects of Production Engineering ◽

10.2478/mape-2018-0049 ◽

2018 ◽

Vol 1 (1) ◽

pp. 385-392

Author(s):

Edyta Brzychczy

Keyword(s):

Process Management ◽

Process Mining ◽

Process Analysis ◽

Process Modelling ◽

Process Models ◽

Sources Of Information ◽

Event Logs ◽

It Systems ◽

Many Sources

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

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Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

The Scientific World JOURNAL ◽

10.1155/2014/298592 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8

Author(s):

Weidong Zhao ◽

Xi Liu ◽

Weihui Dai

Keyword(s):

Process Model ◽

Process Mining ◽

Fitness Function ◽

Control Flow ◽

Process Models ◽

Programming Approach ◽

Event Logs ◽

Process Complexity

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.

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Discovering Business Processes from Email Logs Using fastText and Process Mining

10.20944/preprints202005.0007.v2 ◽

2021 ◽

Author(s):

Yaghoub Rashnavadi ◽

Sina Behzadifard ◽

Reza Farzadnia ◽

Sina Zamani

Keyword(s):

Machine Learning ◽

Text Classification ◽

Business Processes ◽

Process Mining ◽

Real Life ◽

Process Models ◽

Supervised Machine Learning ◽

Implicit Information

Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.

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Discovering Business Processes from Email Logs using fastText and Process Mining

10.36227/techrxiv.12283835.v1 ◽

2020 ◽

Author(s):

Yaghoub rashnavadi ◽

Sina Behzadifard ◽

Reza Farzadnia ◽

sina zamani

Keyword(s):

Machine Learning ◽

Business Processes ◽

Oil And Gas ◽

Process Mining ◽

The Body ◽

Process Models ◽

Supervised Machine Learning ◽

Implicit Information ◽

Oil And Gas Sector

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Modeling clinical activities based on multi-perspective declarative process mining with openEHR’s characteristic

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01323-7 ◽

2020 ◽

Vol 20 (S14) ◽

Author(s):

Haifeng Xu ◽

Jianfei Pang ◽

Xi Yang ◽

Jinghui Yu ◽

Xuemeng Li ◽

...

Keyword(s):

Medical Service ◽

Time Perspective ◽

Process Mining ◽

Control Flow ◽

Healthcare Personnel ◽

Relationship Matrix ◽

Conformance Checking ◽

Stroke Treatment ◽

Medical Facilities

Abstract Background It is significant to model clinical activities for process mining, which assists in improving medical service quality. However, current process mining studies in healthcare pay more attention to the control flow of events, while the data properties and the time perspective are generally ignored. Moreover, classifying event attributes from the view of computers usually are difficult for medical experts. There are also problems of model sharing and reusing after it is generated. Methods In this paper, we presented a constraint-based method using multi-perspective declarative process mining, supporting healthcare personnel to model clinical processes by themselves. Inspired by openEHR, we classified event attributes into seven types, and each relationship between these types is represented in a Constrained Relationship Matrix. Finally, a conformance checking algorithm is designed. Results The method was verified in a retrospective observational case study, which consists of Electronic Medical Record (EMR) of 358 patients from a large general hospital in China. We take the ischemic stroke treatment process as an example to check compliance with clinical guidelines. Conformance checking results are analyzed and confirmed by medical experts. Conclusions This representation approach was applicable with the characteristic of easily understandable and expandable for modeling clinical activities, supporting to share the models created across different medical facilities.

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Experimental Setup and Case Study Example

Applications and Developments in Semantic Process Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-2668-2.ch006 ◽

2020 ◽

pp. 168-180

Keyword(s):

Real Time ◽

Learning Process ◽

Process Mining ◽

Process Models ◽

Experimental Setup ◽

Main Components ◽

The Individual

This chapter represents as a practical follow-up or implementation of the main components of the SPMaAF described in Chapter 5. In the experimental setup, the chapter demonstrates by using the case study of the learning process: the development and application of the semantic-based process mining. Essentially, the chapter looks at how the proposed semantic-based process mining and analysis framework (SPMaAF) is applied to answer real-time questions about any given process domain, as well as the classification of the individual process instances or elements that constitutes process models. This includes the semantic representations and modelling of the learning process in order to allow for an abstraction analysis of the resultant models. The chapter finalizes with a conceptual description of the resultant semantic fuzzy mining approach which is discussed in detail in the next chapter.

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Patient Perspectives to Inform a Health Literacy Educational Program: A Systematic Review and Thematic Synthesis of Qualitative Studies

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16214300 ◽

2019 ◽

Vol 16 (21) ◽

pp. 4300

Author(s):

Margot Jager ◽

Janine de Zeeuw ◽

Janne Tullius ◽

Roberta Papa ◽

Cinzia Giammarchi ◽

...

Keyword(s):

Systematic Review ◽

Health Literacy ◽

Cultural Sensitivity ◽

Data Extraction ◽

Healthcare Providers ◽

Patient Perspectives ◽

Care Process ◽

Patient Centred Care ◽

Thematic Synthesis ◽

Educational Framework

Patient-centred care is tailored to the needs of patients and is necessary for better health outcomes, especially for individuals with limited health literacy (LHL). However, its implementation remains challenging. The key to effectively address patient-centred care is to include perspectives of patients with LHL within the curricula of (future) healthcare providers (HCP). This systematic review aimed to explore and synthesize evidence on the needs, experiences and preferences of patients with LHL and to inform an existing educational framework. We searched three databases: PsychInfo, Medline and Cinahl, and extracted 798 articles. One-hundred and three articles met the inclusion criteria. After data extraction and thematic synthesis, key themes were identified. Patients with LHL and chronic diseases encounter multiple problems in the care process, which are often related to a lack of person-centeredness. Patient perspectives were categorized into four key themes: (1) Support system; (2) Patient self-management; (3) Capacities of HCPs; (4) Barriers in healthcare systems. “Cultural sensitivity” and “eHealth” were identified as recurring themes. A set of learning outcomes for (future) HCPs was developed based on our findings. The perspectives of patients with LHL provided valuable input for a comprehensive and person-centred educational framework that can enhance the relevance and quality of education for (future) HCPs, and contribute to better person-centred care for patients with LHL.

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Discovering Business Processes from Email Logs Using fastText and Process Mining

10.20944/preprints202005.0007.v1 ◽

2020 ◽

Author(s):

Yaghoub Rashnavadi ◽

Sina Behzadifard ◽

Reza Farzadnia ◽

Sina Zamani

Keyword(s):

Machine Learning ◽

Business Processes ◽

Oil And Gas ◽

Process Mining ◽

The Body ◽

Process Models ◽

Supervised Machine Learning ◽

Implicit Information ◽

Oil And Gas Sector

Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

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Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16071138 ◽

2019 ◽

Vol 16 (7) ◽

pp. 1138 ◽

Cited By ~ 6

Author(s):

Robert Andrews ◽

Moe Wynn ◽

Kirsten Vallmuur ◽

Arthur ter Hofstede ◽

Emma Bosley ◽

...

Keyword(s):

Data Quality ◽

Process Mining ◽

Data Extraction ◽

Case Study Research ◽

Poor Quality ◽

Transport Processes ◽

Quality Data ◽

Trauma Patients ◽

Quality Issues

While noting the importance of data quality, existing process mining methodologies (i) do not provide details on how to assess the quality of event data (ii) do not consider how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis, (iii) do not highlight potential impacts of poor quality data on different types of process analyses. As our key contribution, we develop a process-centric, data quality-driven approach to preparing for a process mining analysis which can be applied to any existing process mining methodology. Our approach, adapted from elements of the well known CRISP-DM data mining methodology, includes conceptual data modeling, quality assessment at both attribute and event level, and trial discovery and conformance to develop understanding of system processes and data properties to inform data extraction. We illustrate our approach in a case study involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We describe the detailed preparation for a process mining analysis of retrieval and transport processes (ground and aero-medical) for road-trauma patients in Queensland. Sample datasets obtained from QAS and RSQ are utilised to show how quality metrics, data models and exploratory process mining analyses can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the data extraction and pre-processing stages of a process mining case study to properly align the data with the case study research questions.

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