Process mining for healthcare decision analytics with micro-costing estimations

Leemans, Sander J. J.; Partington, Andrew; Karnon, Jonathan; Wynn, Moe Thandar

doi:10.1016/j.artmed.2022.102473

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…In the process mining domain, [17] used semi-structured interviews to evaluate the face validity of process mining results, but the specific questions used during the interviews were not reported. In the ABS domain, [8] framework, they advocate for further empirical estimation of model parameters through real-world measurements.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Face Validity Assessment of Agent-Based Simulation Models Through Outlier Detection with Process Mining

Bemthuis,

Lazarova-Molnar

2023

Lecture Notes in Computer Science

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When designing simulations, the objective is to create a representation of a real-world system or process to understand, analyze, predict, or improve its behavior. Typically, the first step in assessing the credibility of a simulation model for its intended purpose involves conducting a face validity check. This entails a subjective assessment by individuals knowledgeable about the system to determine if the model appears plausible. The emerging field of process mining can aid in the face validity assessment process by extracting process models and insights from event logs generated by the system being simulated. Process mining techniques, combined with the visual representation of discovered process models, offer a novel approach for experts to evaluate the validity and behavior of simulation models. In this context, outliers can play a key role in evaluating the face validity of simulation models by drawing attention to unusual behaviors that can either raise doubts about or reinforce the model's credibility in capturing the full range of behaviors present in the real world. Outliers can provide valuable information that can help identify concerns, prompt improvements, and ultimately enhance the validity of the simulation model. In this paper, we propose an approach that uses process mining techniques to detect outlier behaviors in agent-based simulation models with the aim of utilizing this information for evaluating face validity of simulation models. We illustrate our approach using the Schelling segregation model.

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Section: Related Workmentioning

confidence: 99%

An Approach for Face Validity Assessment of Agent-Based Simulation Models Through Outlier Detection with Process Mining

Bemthuis,

Lazarova-Molnar

2023

Lecture Notes in Computer Science

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“…Major part of the healthcare data is structured using EHR and the analysis of this is challenging. Therefore the EHR data gets converted into common models [22] . Data mining quantifies the existing data and derives meaningful information.…”

Section: Introductionmentioning

confidence: 99%

High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

Nadakinamani¹,

Reyana²,

Gupta³

et al. 2023

Alexandria Engineering Journal

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“…Finally, the process mining is a data-driven approach that aims to gain insight from "event logs", to evaluate the quality, to discover bottleneck of processes within organization [1], [6]- [9]. Organization involved in research and application of process mining techniques ranging from private companies [10] to public organizations that range from Universities [5], [11] to Health care organizations [12] etc. Analyzing process data and building process models, improvements of the quality of organization processes in terms of cost, time spent, and hum an resources utilized could be reached.…”

Section: Introductionmentioning

confidence: 99%

Next Activity Prediction: An Application of Shallow Learning Techniques Against Deep Learning Over the BPI Challenge 2020

Impedovo,

Pirlo,

Semeraro

2023

IEEE Access

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Business Process Management is a domain that is composed by different research or application areas. Process Mining is one of them and it is a data-driven approach to analyze and improve processes. In Process Mining "next activity prediction" is one of the most important task. The used data are event logs i.e., a timeseries of recorded events. The event logs are mainly processed using deep learning algorithms. In this study, it was proposed the comparison of prediction performance of shallow learning algorithms with a three block Bidirectional LSTM (Bi-LSTM) architecture in predicting the next activity. The algorithms were applied on all the events logs of the BPI Challenge 2020 dataset. Results show that shallow learning algorithms outperform the three-block architecture from a minimum of 1.5 to a maximum of 6 times. This suggest that simpler algorithms may be more effective than the three-block architecture.

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Process mining for healthcare decision analytics with micro-costing estimations

Cited by 8 publications

References 40 publications

An Approach for Face Validity Assessment of Agent-Based Simulation Models Through Outlier Detection with Process Mining

An Approach for Face Validity Assessment of Agent-Based Simulation Models Through Outlier Detection with Process Mining

High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

Next Activity Prediction: An Application of Shallow Learning Techniques Against Deep Learning Over the BPI Challenge 2020

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