Topic Discovery About Economy During COVID-19 Pandemic from Spanish Tweets

Automatic topic discovery from natural language texts has been a challenging and widely studied problem. The ability to discover the topics present in a collection of text documents is essential for information systems. Topic discovery has been used to obtain a compact representation of documents for grouping, classification, and retrieval. Some tasks that can benefit from topic discovery: recommendation systems, tracking misinformation, writing summaries, and text clustering. However, topic discovery from Spanish texts has been somewhat neglected. For this reason, this work proposes analyzing the behavior of topic discovery tasks in texts in Spanish, specifically in tweets about the Mexican economy during the COVID-19 pandemic, under three different approaches. A comparison was conducted, achieving promising results because the topic coherence metric indicates coherent topics. The highest score of 1.22 was obtained using PLSA with 50 topics, concluding that the topics encompassed the study domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Management of diabetic patient profiles using ontologies

In 2019 the International Diabetes Federation estimated that 12.8 million Mexicans had diabetes. The diabetes epidemic ranked second in causes of death in Mexico, a situation that was severely complicated during the second quarter of 2020 with the COVID-19 pandemic. Studies carried out by the Ministry of Public Health showed that the comorbidity of diabetes with COVID-19 has become a risk factor for serious complications, increasing the mortality rate. For this reason, it is necessary to develop personalized information management systems to support medical decision-making considering the specific characteristics of patients in Mexico. Information management of the diabetic patient profile begins with the investigation and registration of the relevant clinical data, data used by the physician to make the diagnosis and determine a personalized treatment. This article reports the development and integration of an ontology model for the management of diabetic patient profiles, incorporating medical ontologies. The results of the evaluation show the feasibility of using this integrated ontology for the management of diabetic patient presenting comorbidities. Likewise, a consistent ontological model is achieved, which complies with extensibility and reusability quality characteristics. © 2020 Universidad Nacional Autonoma de Mexico. All rights reserved.