Discovering Emerging Patterns from Medical Opinions about the Decrease of Autopsies Performed in a Mexican Hospital

Rios-Mendez, Ingrid Aylin; Rodríguez-Mazahua, Lisbeth; Guzmán, José Antonio Palet; Machorro-Cano, Isaac; Peláez-Camarena, Silvestre Gustavo; Romero-Torres, Celia; Contreras, Hilarion Munoz

doi:10.1109/case48305.2020.9216751

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…Although EPM has been applied to solve problems in several domains, to the best of our knowledge, only Ríos-Méndez et al [36] have used it to analyze medical opinions about the decreasing rates of autopsies in hospitals. The difference between [36] and this work is that in [36], the EPs were obtained considering two class labels, justifications for both requesting and rejecting autopsies, while in this work, four different attribute class labels are taken into account to discover EPs: factors influencing family rejection of autopsy, insufficient autopsy rates in hospital settings, appropriate personnel for autopsy requests, and physician-driven reasons for autopsy requests.…”

Section: State Of the Artmentioning

confidence: 99%

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Medical Opinions Analysis about the Decrease of Autopsies Using Emerging Pattern Mining

Machorro-Cano,

Ríos-Méndez,

Palet-Guzmán

et al. 2023

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An autopsy is a widely recognized procedure to guarantee ongoing enhancements in medicine. It finds extensive application in legal, scientific, medical, and research domains. However, declining autopsy rates in hospitals constitute a worldwide concern. For example, the Regional Hospital of Rio Blanco in Veracruz, Mexico, has substantially reduced the number of autopsies at hospitals in recent years. Since there are no documented historical records of a decrease in the frequency of autopsy cases, it is crucial to establish a methodological framework to substantiate any actual trends in the data. Emerging pattern mining (EPM) allows for finding differences between classes or data sets because it builds a descriptive data model concerning some given remarkable property. Data set description has become a significant application area in various contexts in recent years. In this research study, various EPM (emerging pattern mining) algorithms were used to extract emergent patterns from a data set collected based on medical experts’ perspectives on reducing hospital autopsies. Notably, the top-performing EPM algorithms were iEPMiner, LCMine, SJEP-C, Top-k minimal SJEPs, and Tree-based JEP-C. Among these, iEPMiner and LCMine demonstrated faster performance and produced superior emergent patterns when considering metrics such as Confidence, Weighted Relative Accuracy Criteria (WRACC), False Positive Rate (FPR), and True Positive Rate (TPR).

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Section: State Of the Artmentioning

confidence: 99%

“…The data set [15,36] consists of 17 categorical characteristics and a total of 7856 occurrences. Each attribute is shown in Table 1.…”

Section: Data Setmentioning

confidence: 99%

Medical Opinions Analysis about the Decrease of Autopsies Using Emerging Pattern Mining

Machorro-Cano,

Ríos-Méndez,

Palet-Guzmán

et al. 2023

Data

Self Cite

View full text Add to dashboard Cite

show abstract

Discovering Emerging Patterns from Medical Opinions about the Decrease of Autopsies Performed in a Mexican Hospital

Cited by 1 publication

References 22 publications

Medical Opinions Analysis about the Decrease of Autopsies Using Emerging Pattern Mining

Medical Opinions Analysis about the Decrease of Autopsies Using Emerging Pattern Mining

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