Machine Learning Applied to Datasets of Human Activity Recognition: Data Analysis in Health Care.

Background: In order to remain active and productive, older adults with poor health require a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning, which provide safe and comfortable environments and serve as a tool to facilitate the work of family members and workers, both at home and in geriatric homes. Therefore, there is a need to develop technologies to provide these adults autonomy in indoor environments.

Objective: This study aimed to generate a prediction model of daily living activities through classification techniques and selection of characteristics in order to contribute to the development in this area of knowledge, especially in the field of health.

Twelve years of Lyell's Syndrome in the burn unit of São João Hospital Centre.

Stevens-Johnson syndrome (SSJ) and toxic epidermal necrolysis (TEN) correspond to an acute and rare life-threatening mucocutaneous reaction. We describe the etiology, length of stay, comorbidities and mortality of TEN in our hospital in a retrospective study of 12 years. Twenty-one patients were included in the study.

[Development of a multidimensional system for classification and management of health information: applying to clinical information].

Introduction: The large amount of information in the medical area creates management problems, being necessary systematic methods for filing and retrieval. With information on the context of clinical records, methods must integrate controlled biomedical terminologies and desirable characteristics oriented to the structure, content and clinical results. The objective is to test the applicability and capacity for retrieval of a multidimensional system developed for classification and management of health information.