Proposal of a Context-sensitive ECG Collection Mobile Health System for Ambulatory Cardiovascular Diseases.

Background: This study brings innovation oriented to the development of e-health services to improve the performance of healthcare and hospital care in free-living activities. Objective: This article seeks to propose an innovative mobile software that enables the contextualization of clinical data, while integrating the fundamental functionalities and usability criteria of a mobile health (mhealth) system. Methods: We examine the utility of a context-sensitive mobile electrocardiogram (ECG) collection system for the detection of cardiovascular diseases for patients in free-living conditions. Results: We propose an mhealth system that reinforces conventional healthcare systems in which electronic health records are limited by access and processing of data, limiting clinical-medical decision-making. This study highlights the importance of mobile applications for self-care and their usefulness for both patients and healthcare providers. The proposal offers an avenue for the study of contextualized ECG collection methods that can be used to create context-specific actions that reduce false alarms in computer-aided diagnosis of ambulatory ECG. Conclusion: Mobile applications for health care and monitoring improve the quality of life of people with cardiovascular diseases.

An approach for evaluating the bioavailability and risk assessment of potentially toxic elements using edible and inedible plants-the Remance (Panama) mining area as a model.

Mining affects the environment, particularly through the persistence of accumulation of tailings materials; this is aggravated under tropical climatic conditions, which favours the release of potentially toxic elements (PTEs) bioavailable to the local flora and fauna and supposing a risk to human health. The Remance gold mine (Panamá), exploited intermittently for more than 100 years, and has remained derelict for over 20 years. Within the area live farmers who carry out subsistence agriculture and livestock activities. The objective of this study has been to study the transference of PTEs in the local agricultural soil-plants system, with the goal of identifying their bioavailability to perform a human risk assessment. The results obtained of the Bioaccumulation coefficient in local plants show very weak to strong absorption of As (< 0.001-1.50), Hg (< 0.001-2.38), Sb (0.01-7.83), Cu (0.02-2.89), and Zn (0.06-5.32). In the case of Cu in grass (18.3 mg kg-1) and plants (16.9 mg kg-1) the concentrations exceed the maximum authorised value in animal nutrition for ruminants (10 mg kg-1). The risk to human health for edible plants exceeds the non-carcinogenic risk for rice, corn, cassava, and tea leaves for Sb (HQ 19.450, 18.304, 6.075, 1.830, respectively), the carcinogenic risk for Cu (CR = 2.3 × 10-3, 7.7 × 10 -4, 1.1 × 10-3, 1.0 × 10-3, respectively), and the carcinogenic risk for As in rice, corn and tea leaves (CR = 8 × 10-5, 3 × 10-5, 3 × 10-5, respectively). Urgent measures are needed to alleviate these effects.

Study of Text Patterns Found on Social Networks of Mental Health Reactions to COVID-19.

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.

Towards a Model of Big Health Care Data Analytics in Panama: Chronic Kidney Disease.

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.

Environmental challenges related to cyanidation in Central American gold mining; the Remance mine (Panama).

Mine tailings are a potential source of environmental pollution because they typically contain potentially toxic elements (PTEs) and the residue of chemical compounds used during extraction processes. The Remance gold mine (NW Panama) is a decommissioned mine with mining activity records dating from the 1800s and several periods of abandonment. Very little remediation work has been performed, and waste is exposed to climatic conditions. This study aimed to evaluate the PTEs and cyanide contents in mine waste after mining operations ceased some 20 years ago, and to evaluate the degree of pollution and the environmental risks they pose with the use of the Pollution Load Index (PLI) and the Ecological Risk Index (RI). Although the total cyanide (T-CN) concentration (1.4-1.9 mg kg-1) found in most of the study area falls within the limits of gold mining tailing values for American sites (1.5-23 mg kg-1), it is worth noting that the values of the tailings of the last used mining operation exceed it (25.2-518 mg kg-1) and persist at the site. The PLI and RI suggest that the tailings from the mine and mine gallery sediments represent a source of pollution for soils and surrounding areas given their high content of PTEs (As, Cu, Sb, Hg) and T-CN, which pose serious ecological risks for biota. Therefore, it is necessary to draw up a remediation plan for this area.

Medical Prognosis of Infectious Diseases in Nursing Homes by Applying Machine Learning on Clinical Data Collected in Cloud Microservices.

BACKGROUND: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. AIM: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient's well-being and reduce the burden on emergency health system services. METHODS: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. RESULTS: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. CONCLUSIONS: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research.

Ecological and Health Risk Assessments of an Abandoned Gold Mine (Remance, Panama): Complex Scenarios Need a Combination of Indices.

The derelict Remance gold mine is a possible source of pollution with potentially toxic elements (PTEs). In the study area, diverse mine waste has been left behind and exposed to weather conditions, and poses risks for soil, plants and water bodies, and also for the health of local inhabitants. This study sought to perform an ecological and health risk assessment of derelict gold mining areas with incomplete remediation, including: (i) characterizing the geochemical distribution of PTEs; (ii) assessing ecological risk by estimating the pollution load index (PLI) and potential ecological risk index (RI); (iii) assessing soil health by dehydrogenase activity; and iv) establishing non-carcinogenic (HI) and carcinogenic risks (CR) for local inhabitants. Soil health seems to depend on not only PTE concentrations, but also on organic matter (OM). Both indexes (PLI and RI) ranged from high to extreme near mining and waste accumulation sites. As indicated by both the HI and CR results, the mining area poses a health risk for local inhabitants and particularly for children. For this reason, it will be necessary to set up environmental management programs in the areas that are most affected (tailings and surrounding areas) and accordingly establish the best remediation strategies to minimize risks for the local population.

Predicting the Appearance of Hypotension During Hemodialysis Sessions Using Machine Learning Classifiers.

A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

Design and Construction of a NLP Based Knowledge Extraction Methodology in the Medical Domain Applied to Clinical Information.

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.

An Ontology-Based Knowledge Methodology in the Medical Domain in the Latin America: the Study Case of Republic of Panama.

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.

Technologies to better serve the millions of diabetic patients: a holistic, interactive and persuasive ICT model to facilitate self care, in extremely poor rural zones of Central America.

Health indicators express remarkable gaps between health systems at a world-wide level. Countries of the entire world are overflowed by the need of new strategies, methodologies and technologies to better serve the millions of patients, who demand better medical attention. The present archaic and ephemerally systematized systems widen the gap even more than the quality of medical services that should be provided for the millions of diabetic patients. It is therefore necessary to develop highly familiar environments with diabetic patients and their care needs. A Holistic, Interactive and Persuasive ICT model to facilitate self care of patients with diabetes (hIPAPD), is proposed as an innovative technological development in Panama to health optimized treatment for diabetic patients. Three health centers located in the District of Aguadulce, Province of Cocle, located on Panama's Pacific Coast, were selected to validate the model; the area presents extremely poor population, mostly with one daily meal, without any health insurance and with a high illiteracy rate. A series of experiences in the application and validation process are presented and analyzed in order to confirm the application, value and contribution of ICTs in health care in poor regions of Central America.

Scope of information communications technology in the health of diabetes patients in poor rural zones of panama through holistic, interactive, and persuasive model to facilitate self-care of diabetes patients.

BACKGROUND: In Latin America, public health systems that manage and warrant the health of the population lack mechanisms and technological capabilities that enable them to accept and adopt initiatives focused to guide, look after, and improve the quality of life of millions of patients with diabetes who need attention and special care. However, the proposal presented here for a holistic, interactive, and persuasive model to facilitate self-care of diabetes patients (hiPAPD) is the first proposal in Panama, Central America, and the Caribbean Region to develop and implement information communications technology (ICT) platforms for the care of patients with chronic diseases such as diabetes. METHODS: The process of experimentation was initiated with an agreement with all the staff of the project to comply with the international biomedical studies stipulations, having as reference the Declaration of Helsinki of the World Medical Association (Recommendations to Guide to Doctors in Biomedical Research on People). RESULTS: After several months of evaluation and ongoing work the study obtained successful validation of the hiPAPD model. The project had the support of 107 patients with diabetes, their families, friends, doctors, nurses and nursing assistants, and social groups in rural communities. Finally, the project contributed to society with a highly innovative ICT environment that facilitates self-care of diabetes patients without financial resources and health. CONCLUSIONS: A timely health treatment at a decisive moment may be the difference in care for patients. Through the validation process conducted in this research initiative, it was demonstrated that the hiPAPD model, from the perspective of the patient with diabetes, relatives, friends, health workforce (nurses and nursing assistants), doctors, and societal contexts, allowed the improvement of the quality of life of patients with diabetes in poor rural zones of Panama.