Clinical pathways analysis of patients in medical institutions based on hard and fuzzy clustering methods

Prokofyeva, Elizaveta; Zaytsev, Roman

doi:10.17323/2587-814x.2020.1.19.31

Cited by 8 publications

(12 citation statements)

References 26 publications

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“…CP specify the categories of care, activities, and procedures that need to be conducted for a group of patients until they are discharged from the hospital (Aspland et al, 2019). Modelling the processes in a healthcare system plays a large role in understanding its activities and serves as the basis for increasing the efficiency of medical institutions (Prokofyeva and Zaytsev, 2020).…”

Section: Figure 4 Distribution Of Los Based On Age Category and Gendermentioning

confidence: 99%

“…Event log data about movement of patients, in combination with patient characteristics, previous clinical history, current laboratory and clinical test results, can be used to model and predict clinical pathways in a single department or in the entire hospital. Prokofyeva and Zaytsev (2020) analyzed clinical pathways in medical institutions using hard and fuzzy clustering methods and public data. Allen et al (2019) studied significance of machine learning to analyze clinical pathway and enhance clinical audits.…”

Section: Figure 5 Conceptual Architecture Of the Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Machine Learning (ML) (Ngiam and Khor, 2019), using EHR data as an input, has been widely used to study, discover patterns and predict patients' admission rate or demand for healthcare Luo, 2017;Hong, 2018), LoS (Daghistani, 2019;Taleb, 2017), CoT (Bremer et al, 2018;Jödicke et al, 2019), and CP (Kovalchuk et al 2018;Allen et al, 2019;Prokofyeva and Zaytsev, 2020;Funkner, 2017), to mention a few. This paper, hence, aims to describe why coupling machine learning with patient flow simulation is important and proposes a conceptual framework that shows how to integrate machine learning with patient flow simulation.…”

Section: Introductionmentioning

confidence: 99%

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Why Machine Learning Integrated Patient Flow Simulation?

Abuhay¹,

Mamuye²,

Robinson³

et al. 2021

Proceedings of SW21 the OR Society Simulation Workshop

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Tuesday, March 23 Day 3 Wednesday, March 24 Day 4 Thursday, March 25 Day 5 Friday, March 26 Networking break: Speed networking Lunch Break Lunch (1st 30 mins + networking (2nd half hour):(Trying to) Find calm in the chaos (Informal discussion in groups) Lunch (1st 30 mins + networking (2nd half hour): Lunchtime breakout rooms to discuss a simulation topic of interest Lunch (1st 30 mins + networking (2nd half hour)-3 Lunchtime breakout discussion rooms: 1) Your career in Simulation+ 2) WORAN + 3)ORS Orientation

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Section: Figure 4 Distribution Of Los Based On Age Category and Gendermentioning

confidence: 99%

Section: Figure 5 Conceptual Architecture Of the Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Why Machine Learning Integrated Patient Flow Simulation?

Abuhay¹,

Mamuye²,

Robinson³

et al. 2021

Proceedings of SW21 the OR Society Simulation Workshop

View full text Add to dashboard Cite

show abstract

“…Indicators of the sum of squares of distances between points within the cluster and the average width of the silhouette [24,25] allow us to assess the quality of clustering. For the sum of squared distances, the "elbow bend" method is used [22,26] to determine the optimal number of clusters, and the local maximum of the silhouette width value allows you to select the number of clusters with the best separation.…”

Section: Economic Effectmentioning

confidence: 99%

Structuring advertising campaign costs considering the asymmetry of users’ interests

Kislyakov¹

2020

Bus. Inform.

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This work is devoted to the highly topical problem of structuring costs for contextual and targeted advertising on the Internet. The choice of the ad campaign financing structure is considered from the point of view of violating the principle of symmetry of user interest in ads. The purpose of this work is to develop a methodology for structuring advertising campaign costs based on cluster analysis, taking into account the asymmetry of user interest in advertising. The key feature of the research is the description of the possibility of using the asymmetry of user interest in application solutions, such as online advertising. The Gini coefficient is used as an indicator of the degree of imbalance in the manifestation of a feature in clustering, and the features of using the lift coefficient and the Lorentz curve to evaluate the effectiveness of contextual and targeted advertising for various groups of customers are also considered. Using the Gini index and cluster analysis, you can analyze the possibilities of increasing ad revenue and compare it with the absence of any policy for structuring advertising costs. Identifying such patterns in consumer groups allows you to identify the main directions of product development and customer interest in it. The method described here should be used to improve the effectiveness of banner advertising and clustering algorithms. This approach does not improve banner clickability, but allows you to implement an individual approach to advertising products with the current number of clicks and more effectively structure the cost of various types of advertising.

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Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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Process mining (PM) is a well-known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.

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Clinical pathways analysis of patients in medical institutions based on hard and fuzzy clustering methods

Cited by 8 publications

References 26 publications

Why Machine Learning Integrated Patient Flow Simulation?

Why Machine Learning Integrated Patient Flow Simulation?

Structuring advertising campaign costs considering the asymmetry of users’ interests

Process mining applications in the healthcare domain: A comprehensive review

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