Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

A clinical recommendation system to maternity care

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, it was developed and implemented in Centro Hospitalar do Porto a pre-triage system in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.

Step towards to improve the voluntary interruption of pregnancy by means of business intelligence

With the implementation of Information and Communication Technologies in the health sector, it became possible the existence of an electronic record of information for patients, enabling the storage and the availability of their information in databases. However, without the implementation of a Business Intelligence (BI) system, this information has no value. Thus, the major motivation of this paper is to create a decision support system that allows the transformation of information into knowledge, giving usability to the stored data. The particular case addressed in this chapter is the Centro Materno Infantil do Norte, in particular the Voluntary Interruption of Pregnancy unit. With the creation of a BI system for this module, it is possible to design an interoperable, pervasive and real-time platform to support the decision-making process of health professionals, based on cases that occurred. Furthermore, this platform enables the automation of the process for obtaining key performance indicators that are presented annually by this health institution. In this chapter, the BI system implemented in the VIP unity in CMIN, some of the KPIs evaluated as well as the benefits of this implementation are presented.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.

Repair of an Atherosclerotic Coronary Artery Aneurysm by Implantation of a Coronary Covered Stent

An atherosclerotic aneurysm of the right coronary artery complicated by a recent myocardial infarction was successfully treated with coronary artery stenting, using a device consisting of 2 stents with a layer of expandable polytetrafluorethylene (PTFE) placed between them. A follow-up angiograph 5 months after the procedure showed sustained initial results.

Acute myocardial infarction: clinical and epidemiological profile and factors associated with in-hospital death in the municipality of Rio de Janeiro

OBJECTIVE: To study the factors associated with the risk of in-hospital death in acute myocardial infarction in the Brazilian public health system in Rio de Janeiro, Brazil. METHODS: Sectional study of a sample with 391 randomly drawn medical records of the hospitalizations due to acute myocardial infarction recorded in the hospital information system in 1997. RESULTS: The diagnosis was confirmed in 91.7% of the cases; 61.5% males; age = 60.2 ± 2.4 years; delta time until hospitalization of 11 hours; 25.3% were diabetic; 58.1% were hypertensive; 82.6% were in Killip I class. In-hospital mortality was 20.6%. Thrombolysis was used in 19.5%; acetylsalicylic acid (ASA) 86.5%; beta-blockers 49%; angiotensin-converting enzyme (ACE) inhibitors 63.3%; calcium channel blockers 30.5%. Factors associated with increased death: age (61-80 years: OR=2.5; > 80 years: OR=9.6); Killip class (II: OR=1.9; III: OR=6; IV: OR=26.5); diabetes (OR=2.4); ventricular tachycardia (OR=8.5); ventricular fibrillation (OR=34); recurrent ischemia (OR=2.7). The use of ASA (OR=0.3), beta-blockers (OR=0.3), and ACE inhibitors (OR=0.4) was associated with a reduction in the chance of death. CONCLUSION: General lethality was high and some interventions of confirmed efficacy were underutilizated. The logistic model showed the beneficial effect of beta-blockers, and ACE inhibitors on the risk of in-hospital death.

Volumosas fístulas de ambas coronárias para a artéria pulmonar: embolização percutânea com micro-molas e balões destacáveis

Mulher de 43 anos, sintomática (dispnéia e palpitações), apresentava múltiplas fístulas de alto débito de ambas coronárias para a artéria pulmonar, embolizadas percutaneamente com micro-molas de liberação controlada e balões destacáveis, com sucesso.

Cardiomiopatia de Takotsubo: uma causa rara de choque cardiogênico simulando infarto agudo do miocárdio

Cardiomiopatia de Takotsubo é uma causa rara de aneurisma ventricular esquerdo agudo, na ausência de coronariopatia, só recentemente descrita na literatura mundial. Os sintomas podem assemelhar-se aos do infarto agudo do miocárdio com dor torácica típica. A imagem do balonamento ventricular sugestivo de haltere ou "Takotsubo" (dispositivo utilizado no Japão para prender Octopus) é característico desta nova síndrome e usualmente há desaparecimento do movimento discinético até o 18º dia do início dos sintomas, em média.

Optimización microestructural y desarrollo de textura en imanes de Nd-Fe-B producidos por la técnica HDDR

El proceso de HDDR desde su aparición en 1989, como una nueva alternativa de producción de superimanes de Nd-Fe-B, ha generado un creciente interés tanto a nivel de los grupos de investigación y desarrollo como de las industrias. Este método resulta sumamente atractivo puesto que ofrece la posibilidad de producir polvos que pueden utilizarse para el conformado de imanes por ligado o inyección mediante un proceso que involucra un número reducido de operaciones. Sin embargo, la complejidad de los fenómenos fisicoquímicos involucrados ha originado un retardo significativo en su implementación a nivel productivo. El desafío tecnológico vigente es generar el conjunto de conocimientos que sirvan de sustento a la ingeniería de este proceso de indudables perspectivas económicas. (...) Objetivo general: Desarrollar un método de producción de imanes de tierras raras de bajo costo, con viabilidad de implementación industrial en Argentina. Objetivo específico: Contribuir a la interpretación del origen de la anisotropía en polvos de Nd-Fe-B producidos por el método HDDR.

Efecto de los campos magnéticos en la solidificación de fluidos orgánicos

La aplicación de los campos magnéticos al acondicionamiento de fluidos, fundamentalmente agua o hidrocarburos, se ha difundido en occidente en los últimos años. Existen en la actualidad diversos dispositivos disponibles comercialmente para eliminar depósitos de sales en aguas de duras, o atenuar la deposición de parafinas en los procesos de extracción y conducción de crudo de petróleo, y de algunos de sus derivados tales como los aceites minerales, el gas-oil, etc. Es reciente la publicación de estudios científicos que reportan los efectos de campos magnéticos intensos sobre las propiedades de precipitación de carbonatos. En los hidrocarburos, por otro lado, se ha informado la prolongación de los períodos de bombeo sin interrupción por taponamiento gracias a la presencia de dispositivos que inducen campos magnéticos en el interior de las tuberías. Sin embargo no se dispone hasta el momento de explicaciones científicas del fenómeno que permitan predecir y cuantificar los efectos descriptos. Por otro lado el diseño racional de dispositivos de tratamiento magnético requiere del cálculo de propiedades de equilibrio relevantes, tales como por ejemplo las temperaturas de formación de sólidos. En este contexto el abordaje termodinámico del problema implica un paso esencial en la conformación de un marco fundamental para su análisis, cuantificación y delimitación. La etapa de investigación que se pretende abarcar en el desarrollo del presente proyecto se focalizará en el análisis termodinámico del equilibrio sólido/líquido de sistemas orgánicos multicomponentes, en presencia de campos magnéticos estacionarios. Objetivo general * Contribuir a la interpretación del origen de los efectos de los campos magnéticos en el equilibrio sólido/líquido de sustancias orgánicas. Objetivos específicos * Elaboración de un modelo termodinámico para descripción del equilibrio sólido/líquido de mezclas multicomponentes parafinas/solventes, en presencia de campos magnéticos estacionarios. * Aplicación del modelo a sistemas hidrocarburo/parafinas.