Model discovery, and replay fitness validation using inductive mining techniques in medical training of CVC surgery
| Autor: | Gopi Battineni, Nalini Chintalapudi, Francesco Amenta |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Applied Computing and Informatics, Vol 18, Iss 3/4, Pp 245-255 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2210-8327 2634-1964 |
| DOI: | 10.1016/j.aci.2020.01.001/full/pdf |
| Popis: | Medical training is a foundation on which better health care quality has been built. Freshly graduated doctors have required a good knowledge of practical competencies, which demands the importance of medical training activities. As of this, we propose a methodology to discover a process model for identifying the sequence of medical training activities that had implemented in the installation of a Central Venous Catheter (CVC) with the ultrasound technique. A dataset with twenty medical video recordings were composed with events in the CVC installation. To develop the process model, the adoption of process mining techniques of infrequent Inductive Miner (iIM) with a noise threshold value of 0.3 had done. A combination of parallel and sequential events of the process model was developed. Besides, process conformance was validated with replay fitness value about 61.1%, and it provided evidence that four activities were not correctly fit in the process model. The present study can assist upcoming doctors involved in CVCs surgery by providing continuous training and feedback on better patient care. |
| Databáze: | Directory of Open Access Journals |
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