Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Avaliação de um processo de aconselhamento na construção de carreira: estudo de caso

Dissertação de mestrado integrado em Psicologia

The music of language: an ERP investigation of the effects of musical training on emotional prosody processing

Recent studies have demonstrated the positive effects of musical training on the perception of vocally expressed emotion. This study investigated the effects of musical training on event-related potential (ERP) correlates of emotional prosody processing. Fourteen musicians and fourteen control subjects listened to 228 sentences with neutral semantic content, differing in prosody (one third with neutral, one third with happy and one third with angry intonation), with intelligible semantic content (semantic content condition--SCC) and unintelligible semantic content (pure prosody condition--PPC). Reduced P50 amplitude was found in musicians. A difference between SCC and PPC conditions was found in P50 and N100 amplitude in non-musicians only, and in P200 amplitude in musicians only. Furthermore, musicians were more accurate in recognizing angry prosody in PPC sentences. These findings suggest that auditory expertise characterizing extensive musical training may impact different stages of vocal emotional processing.

FOSTER: facilitate open science training for european research

FOSTER aims to support different stakeholders, especially young researchers, in adopting open access in the context of the European Research Area (ERA) and in complying with the open access policies and rules of participation set out for Horizon 2020 (H2020). FOSTER establish a European-wide training programme on open access and open data, consolidating training activities at downstream level and reaching diverse disciplinary communities and countries in the ERA. The training programme includes different approaches and delivery options: elearning, blearning, self-learning, dissemination of training materials/contents, helpdesk, face-to-face training, especially training-the-trainers, summer schools, seminars, etc.

Assistive locomotion strategies for active lower limb devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Fueling the bio-economy: european culture collections and microbiology education and training

A survey of European Microbial Biological Resource Centers and their users provided an overview on Microbiology education and training. The results identified future increases in demand despite several shortcomings and gaps in the current offer. Urgent adjustments are needed to match users' needs, integrate innovative programs, and adopt new technologies.

O contributo da narrativa digital para o desenvolvimento da fala num caso de Rubinstein-Taybi: estudo de caso

Dissertação de mestrado em Ciências da Educação (área de especialização em Tecnologia Educativa)

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

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.

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%.

Retrospective Study of the Survival of Patients who Underwent Cardiopulmonary Resuscitation in an Intensive Care Unit

OBJECTIVE: To evaluate clinical and evolutive characteristics of patients admitted in an intensive care unit after cardiopulmonary resuscitation, identifying prognostic survival factors.METHODS: A retrospective study of 136 patients admitted between 1995 and 1999 to an intensive care unit, evaluating clinical conditions, mechanisms and causes of cardiopulmonary arrest, and their relation to hospital mortality.RESULTS: A 76% mortality rate independent of age and sex was observed. Asystole was the most frequent mechanism of death, and seen in isolation pulmonary arrest was the least frequent. Cardiac failure, need for mechanical ventilation, cirrhosis and previous stroke were clinically significant (p<0.01) death factors.CONCLUSION: Prognostic factors supplement the doctor's decision as to whether or not a patient will benefit from cardiopulmonary resuscitation.

Unsupervised Rehabilitation: effects of Exercise Training over the Long Run

OBJECTIVE: To assess the safety and efficacy of unsupervised rehabilitation (USR) in the long run in low-risk patients with coronary artery disease. METHODS: We carried out a retrospective study with 30 patients divided into: group I (GI) - 15 patients from private clinics undergoing unsupervised rehabilitation; group II (GII) - control group, 15 patients from ambulatory clinic basis, paired by age, sex, and clinical findings. GI was stimulated to exercise under indirect supervision (jogging, treadmill, and sports). GII received the usual clinical treatment. RESULTS: The pre- and postobservation values in GI were, respectively: VO2peak (mL/kg/min), 24±5 and 31± 9; VO2 peak/peak HR: 0.18±0.05 and 0.28±0.13; peak double product (DP peak):26,800±7,000 and 29,000 ± 6,500; % peak HR/predicted HRmax: 89.5±9 and 89.3±9. The pre- and post- values in GII were: VO2 peak (mL/kg/min), 27± 7 and 28±5; VO2 peak/peak HR: 0.2±0.06 and 0.2± 0.05; DP peak: 24,900±8,000 and 25,600± 8,000, and % peak HR/predicted HRmax: 91.3±9 and 91.1± 11. The following values were significant: preobservation VO2peak versus postobservation VO2peak in GI (p=0.0 063); postobservation VO2peak in GI versus postobservation VO2peak in GII (p=0.0045); postobservation VO2 peak/peak HR GI versus postobservation peak VO2/peak HR in GII (p=0.0000). The follow-up periods in GI and GII were, respectively, 41.33± 20.19 months and 20.60±8.16 months (p<0.05). No difference between the groups was observed in coronary risk factors, therapeutic management, or evolution of ischemia. No cardiovascular events secondary to USR were observed in 620 patient-months. CONCLUSION: USR was safe and efficient, in low-risk patients with coronary artery disease and provided benefits at the peripheral level.

Benefits of exercise training in the treatment of heart failure: study with a control group

OBJECTIVE: Exercise training programs have been proposed as adjuncts to treatment of heart failure. The effects of a 3-month-exercise-training-program with 3 exercise sessions per week were assessed in patients with stable systolic chronic heart failure. METHODS: We studied 24 patients with final left ventricle diastolic diameter of 70±10mm and left ventricular ejection fraction of 37±4%. Mean age was 52±16 years. Twelve patients were assigned to an exercise training group (G1), and 12 patients were assigned to a control group (G2). Patients underwent treadmill testing, before and after exercise training, to assess distance walked, heart rate, systolic blood pressure, and double product. RESULTS: In G2 group, before and after 3 months, we observed, respectively distance walked, 623±553 and 561± 460m (ns); peak heart rate, 142±23 and 146± 33b/min (ns); systolic blood pressure, 154±36 and 164±26 mmHg (ns); and double product, 22211± 6454 and 24293±7373 (ns). In G1 group, before and after exercise, we observed: distance walked, 615±394 and 970± 537m (p<0.003) peak heart rate, 143±24 and 143±29b/min (ns); systolic blood pressure, 136±33 and 133±24 mmHg (ns); and double product, 19907± 7323 and 19115±5776, respectively. Comparing the groups, a significant difference existed regarding the variation in the double product, and in distance walked. CONCLUSION: Exercise training programs in patients with heart failure can bring about an improvement in physical capacity.