Optimal Process Mining for Large and Complex Event Logs

Prodel, Martin; Augusto, Vincent; Jouaneton, B.; Lamarsalle, Ludovic; Xie, Xiaolan

doi:10.1109/tase.2017.2784436

Cited by 38 publications

(39 citation statements)

References 20 publications

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“…The Prodel et al algorithm has been implemented on the French PMSI hospital database to discover patterns in patient pathways for cardiovascular disease before and after implantation of a cardioverter defibrillator. 5 As well as confirming existing cardiologist knowledge around the main steps in the patient pathway, the analysis also discovered additional outcomes related to cardiomyopathy, tachycardia, and ischemic disease.…”

Section: Introductionsupporting

confidence: 62%

“…The Prodel et al metaheuristic algorithm, including data pre-processing and model construction, was implemented in Python 3.7 (further details are provided in Prodel et al, 2018). 5 …”

Section: Methodsmentioning

confidence: 99%

“…The search-based genetic miner approach, for example, 8 is limited by its scalability and practical ability to deal with the large datasets commonly seen in healthcare—where patient numbers and clinical events may number hundreds of thousands. Prodel et al 5 have developed a proprietary search-based pathway discovery analysis procedure based on a metaheuristic optimization algorithm designed for healthcare that is scalable to population size. This metaheuristic approach uses a combination of Monte Carlo sampling and tabu search to overcome the complexity related to large event logs and uses a “replayability score” to determine the “fitness” of the discovered process model under specific size constraints (event nodes and edges included in the process model).…”

Section: Introductionmentioning

confidence: 99%

“…a hospital stay); (2) each event can be linked to a consistent “trace” (eg, the patient ID); and (3) events have a time stamp allowing them to be sequentially ordered. 5 HES data comply with each of these requirements, making it a suitable data source for PM analyses; however, to the best of our knowledge, no PM studies using HES data have been reported to date.…”

Section: Introductionmentioning

confidence: 99%

“…The current study used the Prodel et al 5 metaheuristic optimization algorithm to perform a “bow-tie” pathway discovery analysis on event log data extracted from the HES database in order to:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

“Bow-tie” optimal pathway discovery analysis of sepsis hospital admissions using the Hospital Episode Statistics database in England

Oliveira

Prodel²,

Lamarsalle³

et al. 2020

JAMIA Open

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Objective The “Bow-tie” optimal pathway discovery analysis uses large clinical event datasets to map clinical pathways and to visualize risks (improvement opportunities) before, and outcomes after, a specific clinical event. This proof-of-concept study assesses the use of NHS Hospital Episode Statistics (HES) in England as a potential clinical event dataset for this pathway discovery analysis approach. Materials and Methods A metaheuristic optimization algorithm was used to perform the “bow-tie” analysis on HES event log data for sepsis (ICD-10 A40/A41) in 2016. Analysis of hospital episodes across inpatient and outpatient departments was performed for the period 730 days before and 365 days after the index sepsis hospitalization event. Results HES data captured a sepsis event for 76 523 individuals (>13 years), relating to 580 000 coded events (across 220 sepsis and non-sepsis event classes). The “bow-tie” analysis identified several diagnoses that most frequently preceded hospitalization for sepsis, in line with the expectation that sepsis most frequently occurs in vulnerable populations. A diagnosis of pneumonia (5 290 patients) and urinary tract infections (UTIs; 2 057 patients) most often preceded the sepsis event, with recurrent UTIs acting as a potential indicative risk factor for sepsis. Discussion This proof-of-concept study demonstrates that a “bow-tie” pathway discovery analysis of the HES database can be undertaken and provides clinical insights that, with further study, could help improve the identification and management of sepsis. The algorithm can now be more widely applied to HES data to undertake targeted clinical pathway analysis across multiple healthcare conditions.

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Section: Introductionsupporting

confidence: 62%

“…The Prodel et al metaheuristic algorithm, including data pre-processing and model construction, was implemented in Python 3.7 (further details are provided in Prodel et al, 2018). 5 …”

Section: Methodsmentioning

confidence: 99%