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Background: SARS-CoV-2 is an infectious disease caused by the coronavirus that was first reported in December 2019 in China and immediately spread around the world causing a pandemic, which has caused countless deaths and cases in global health. Mental health has not gone untouched by this pandemic; due to the lockdown and the vast amounts of information disseminated, the Panamanian population has begun to feel the collateral effects. Objective: We propose classifying tweets using a machine learning (ML) and deep learning (DL) approach and pattern search to make recommendations to the emotional and psychological reactions of the Panamanian population. Methods: Our study has been carried out with a corpus in spanish extracted from X for the automatic classification of texts, from which we have categorized, through the ML&DL approach, the tweets about Covid-19 in Panama, in order to know if the population has suffered any mental health effects. Results: We can say that the ML models provide competitive results in terms of automatic identification of texts with an accuracy of 90%. Conclusion: X is a social network and an important information channel where you can explore, analyze and organize opinions to make better decisions. Text mining and patron search are a natural language processing (NLP) task that, using ML&DL algorithms, can integrate innovative strategies into information and communication technologies.
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Background: A growing number of mobile applications have been designed for the chronic disease patient as the primary user. Mobile health applications for self-care have the potential to help patients living with chronic conditions such as kidney disease, and can be used to manage aspects such as the consumption of substances that are harmful to health. Chronic kidney disease causes significant morbidity throughout Panama, and is also responsible for an increase in cardiovascular disease. Objective: In this paper, we present a review of the applications offered by the Android store, based on a search and selection of the most efficient options that fulfill a set of criteria and functionalities. Methods: We evaluate a big health data model in terms of its usefulness for studies, research and projections of Panamanian patients with this chronic disease. Results and Discusion: We present a mobile application based on the most important standards and functionalities for the Panamanian population affected by this disease. Our analysis also highlights the importance of mobile applications for the self-care of chronic diseases and their usefulness to both patients and health care providers, since they can support better health habits and give good results in terms of following a diet, promoting a healthy lifestyle, and encouraging physical activity. The analysis presented here will form the basis for the development of an application that will be simple, user-friendly and powerful, in the sense that it will empower patients with the resources they need for self-care. . Conclusion: Mobile applications are found to show promise for the self-care of chronic conditions, and can improve the quality of life of Panamanian patients. In addition, we intend to develop a data repository for scientific research within Central America.
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INTRODUCTION: Nowadays in Panama, there is a lot of patient information stored in textual form which cannot be manipulated to manage adequate knowledge. There are multiple resources created to represent knowledge, including specialized glossaries, ontologies, among others. The ontologies are an important part within the scope of the recovery and organization of the information and the semantic web. Also in recent works they are used in applications of natural language processing (NLP), as a knowledge base. AIM: This research was conducted with the aim of creating a methodology that allows from a text written in NL, extract the necessary elements using NLP tools and with them create a knowledge base represented by one domain ontology and extract knowledge to help medical specialists. MATERIAL AND METHODS: In this study we carried out a methodology that allows the extraction of knowledge of patient clinical records, general medicine and palliative care, in order to show relevant knowledge elements to specialists. The methodology was validated with a data corpus of approximately 200 patient records. CONCLUSION: We have created a knowledge representation methodology, combining NLP techniques and tools and the automatic instantiation of an ontology, which can serve as a software agent for other applications or used to visualize the patient's clinical information. The study was validated using the traditional metrics of information retrieval systems precision, recall, F-measure obtaining excellent results, and can be used as a software agent or methodology for the development of information extraction software systems in the medical domain.
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OBJECTIVES: This research presents the design and development of a software architecture using natural language processing tools and the use of an ontology of knowledge as a knowledge base. METHODS: The software extracts, manages and represents the knowledge of a text in natural language. A corpus of more than 200 medical domain documents from the general medicine and palliative care areas was validated, demonstrating relevant knowledge elements for physicians. RESULTS: Indicators for precision, recall and F-measure were applied. An ontology was created called the knowledge elements of the medical domain to manipulate patient information, which can be read or accessed from any other software platform. CONCLUSIONS: The developed software architecture extracts the medical knowledge of the clinical histories of patients from two different corpora. The architecture was validated using the metrics of information extraction systems.