| dc.contributor.author | Olmos Vallejo, Araceli | |
| dc.contributor.author | Rodríguez Mazahua, Lisbeth | |
| dc.contributor.author | Palet Guzmán, José Antonio | |
| dc.contributor.author | Machorro Cano, Isaac | |
| dc.contributor.author | Alor-Hernández, Giner | |
| dc.contributor.author | Sánchez Cervantes, José Luis | |
| dc.date.accessioned | 2024-01-24T00:55:35Z | |
| dc.date.available | 2024-01-24T00:55:35Z | |
| dc.date.issued | 2023-08-22 | |
| dc.identifier.isbn | 978-9978-10-844-4 | |
| dc.identifier.uri | http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/807 | |
| dc.description | An autopsy is a recognized procedure for achieving continuous improvement in the quality of medical work despite its worldwide decline, while data mining is the process of finding patterns, anomalies, or correlations between data in a data set. Supervised descriptive rule discovery (SDRD) brings together the two types of tasks that exist in data mining, i.e., both descriptive and predictive areas. The objective is to describe data with respect to a property of interest. In this paper, we provide the results of the analysis of 39 articles related to SDRD to select the adequate techniques and technologies to design the architecture of a module for the comparison of medical opinions related to the decrease ot autopsies in Mexican hospitals. In this way, we seek to address the current problem of the decrease in the number of autopsies in the country, which resulted in the interest of the director of
the Pathology area of the Hospital Regional of Río Blanco (H.R.R.B.) in designing and applying a survey to physicians to know the causes of this autopsy decline to take actions that increase this study. The SDRD works were analyzed following a methodology wtih three stages: search, selection and analysis, to conclude that the technique that provides the ability to perform all the features proposed in this work is subgroup discovery (SD), therefore, the proposed module architecture was designed considering SD to compare the results obtained in the H.R.R.B. with those of other hospitals. | es |
| dc.description.abstract | An autopsy is a recognized procedure for achieving continuous improvement in the quality of medical work despite its worldwide decline, while data mining is the process of finding patterns, anomalies, or correlations between data in a data set. Supervised descriptive rule discovery (SDRD) brings together the two types of tasks that exist in data mining, i.e., both descriptive and predictive areas. The objective is to describe data with respect to a property of interest. In this paper, we provide the results of the analysis of 39 articles related to SDRD to select the adequate techniques and technologies to design the architecture of a module for the comparison of medical opinions related to the decrease ot autopsies in Mexican hospitals. In this way, we seek to address the current problem of the decrease in the number of autopsies in the country, which resulted in the interest of the director of
the Pathology area of the Hospital Regional of Río Blanco (H.R.R.B.) in designing and applying a survey to physicians to know the causes of this autopsy decline to take actions that increase this study. The SDRD works were analyzed following a methodology wtih three stages: search, selection and analysis, to conclude that the technique that provides the ability to perform all the features proposed in this work is subgroup discovery (SD), therefore, the proposed module architecture was designed considering SD to compare the results obtained in the H.R.R.B. with those of other hospitals. | es |
| dc.description.sponsorship | Tecnológico Nacional de México (TecNM)
Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) | es |
| dc.language.iso | en | es |
| dc.publisher | Editorial Universitaria Abya-Yala | es |
| dc.subject | Supervised Descriptive Rule Discovery | es |
| dc.subject | Visualization | es |
| dc.subject | Contrast Sets | es |
| dc.subject | Subgroup Discovery | es |
| dc.title | Application of Supervised Descriptive Rule Discovery Methods: Review and Architecture | es |
| dc.type | Article | es |
