<p class="0abstract"><span lang="EN-US">Diabetes is a silent disease, the number of people who suffer from it increases daily, it is unfortunate that many young people develop this condition and do not know that they suffer from it. So much so that this disease is the fifth cause of death in Panama. Using software technologies applied to areas such as health every day is increasing. Scientific research in health areas, as well as the development of new technologies that involve smartphones and sensors, is making health self-care possible. Currently, interest in mobile health (mHealth) applications for disease self-care is growing. The innovation of technological tools associated with artificial intelligence is increasing every day. Among its most radical trends is machine learning, whose function is to develop techniques that allow computers to learn. This learning occurs through the data that can be provided to the algorithms responsible for categorizing. Therefore, this research aims to analyze mobile applications specifically those focused on diabetes, to propose an emerging systematic model of medical care for self-management of patients with diabetes and, finally, achieve a reliable data set with Panamanian patient data to apply machine-learning models and see how much we can help Panamanian doctors. </span></p>

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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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Con la evolución del Internet, hay una gran cantidad de información presente en la web como lo son las opiniones de los usuarios o  consumidores sobre diversos contextos ya sea para expresar su conformidad o inconformidad sobre un producto o servicio recibido, así como la opinión de un artículo comprado o sobre la gestión que realiza alguna persona. Debido a la gran cantidad de opiniones, comentarios y sugerencias de los usuarios, es muy importante explorar, analizar y organizar sus puntos de vista para tomar mejores decisiones. El análisis de sentimientos es una tarea de procesamiento de lenguaje natural y extracción de información que identifica las opiniones de los usuarios explicadas en forma de comentarios positivos, negativos o neutrales. Varias técnicas pueden ser utilizadas para este fin, por ejemplo el uso de diccionarios léxicos que ha sido muy utilizada y recientemente la utilización de la inteligencia artificial específicamente algoritmos supervisados. En este documento, se propone la utilización de técnicas de algoritmos supervisados para observar su utilización y ver el rendimiento de diferentes modelos de algoritmos supervisados para medir la efectividad en la clasificación de un conjunto de datos.