Aggregating community resources of care and assistance services for the elderly population

Changes in population age structure are a major concern and represent a priority in the agendas and policies of the developed world, which are demanding for renewed models of social and healthcare as well as assistance services to the elderly population. Studies indicate that as far as possible these types of services should desirably be provided at the user’s home, and that ICT-based solutions can have tremendous impact on the delivery of new services. This paper highlight and discusses some of the main results of a project undertaken in a Portuguese Municipality that demonstrates the potential contribution of an e-Marketplace of care and assistance services to the well-being of elderly people. Studies undertaken allowed identifying the main services that should be provided by such e-Marketplace (termed GuiMarket), the relevance that the population grant to this platform and, conversely, the fact that the Digital Divide phenomena influences the potential utilization of this project (and alike projects). The findings support that there is a strong relation between age and qualifications, and between access to ICT and the intended use of GuiMarket.

Child abuse monitor system model: a health care critical knowledge monitor system

The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.

Clustering-based approach for categorizing pregnant women in obstetrics and maternity care

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

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.

Quality of institutional care and early childhood development

Institutional rearing adversely affects children’s development, but the extent to which specific characteristics of the institutional context and the quality of care provided contribute to problematic development remains unclear. In this study, 72 preschoolers institutionalised for at least 6 months were evaluated by their caregiver using the Child Behavior Checklist and the Disturbances of Attachment Interview. Distal and proximate indices of institutional caregiving quality were assessed using both staff reports and direct observation. Results revealed that greater caregiver sensitivity predicted reduced indiscriminate behaviour and secure-base distortions. A closer relationship with the caregiver predicted reduced inhibited attachment behaviour. Emotional and behavioural problems proved unrelated to caregiving quality. Results are discussed in terms of (non)-shared caregiving factors that influence institutionalised children’s development.

Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

Breastfeeding is negatively affected by prenatal depression and reduces postpartum depression

Background. This prospective cohort study explored the effects of prenatal and postpartum depression on breastfeeding and the effect of breastfeeding on postpartum depression. Method. The Edinburgh Postpartum Depression Scale (EPDS) was administered to 145 women at the first, second and third trimester, and at the neonatal period and 3 months postpartum. Self-report exclusive breastfeeding since birth was collected at birth and at 3, 6 and 12 months postpartum. Data analyses were performed using repeated-measures ANOVAs and logistic and multiple linear regressions. Results. Depression scores at the third trimester, but not at 3 months postpartum, were the best predictors of exclusive breastfeeding duration (β =−0.30, t=−2.08, p<0.05). A significant decrease in depression scores was seen from childbirth to 3 months postpartum in women who maintained exclusive breastfeeding for53 months (F1,65 =3.73, p<0.10, ηp 2 =0.05). Conclusions. These findings suggest that screening for depression symptoms during pregnancy can help to identify women at risk for early cessation of exclusive breastfeeding, and that exclusive breastfeeding may help to reduce symptoms of depression from childbirth to 3 months postpartum.

The state of art of biological processes in paternal care

This review of the state of art aimed to present the most recent data on neuronal, neurochemical, hormonal and genetic bases of paternal care using MEDLINE and PsycInfo databases (1970-2013). An integrated model of biological substrates that assist men in the transition to fatherhood is presented. Guided by a genetic background, hypothalamic-midbrain-limbic-paralimbic-cortical circuits were found to be activated in fathers when infant stimuli are presented. A set of specifi c neuropeptides and steroid hormones are produced and seem to be related to brain activation, potentiating the paternal phenotype. Together, genetic, brain and hormonal processes suggest the existence of biological bases of paternal care in humans, activated and enhanced by infant stimuli and responsive to variations in the father-infant relationship.

Mother’s depression at childbirth does not contribute to the effects of antenatal depression on neonate’s behavioral development

Background: Maternal depression is a worldwide phenomenon that has been linked to adverse developmental outcomes in neonates. Aims: To study the effect of antenatal depression (during the third trimester of pregnancy) on neonate behavior, preference, and habituation to both the mother and a stranger’s face/voice. To analyze mother’s depression at childbirth as a potential mediator or moderator of the relationship between antenatal depression and neonate behavioral development. Method: A sample of 110 pregnant women was divided in 2 groups according to their scores on the Edinburgh Postnatal Depression Scale during pregnancy (EPDS; ≥10, depressed; <10, non-depressed). In the first 5 days after birth, neonatal performance on the Neonatal Behavioral Assessment Scale (NBAS) and in the ‘Preference and habituation to the mother’s face/voice versus stranger’ paradigm was assessed; each mother filled out an EPDS. Results: Neonates of depressed pregnant women, achieved lower scores on the NBASs (regulation of state, range of state, and habituation); did not show a visual/auditory preference for the mother’s face/voice; required more trials to become habituated to the mother’s face/voice; and showed a higher visual/auditory preference for the stranger’s face/voice after habituation compared to neonates of non-depressed pregnant women. Depression at childbirth does not contribute to the effect of antenatal depression on neonatal behavioral development. Conclusion: Depression even before childbirth compromises the neonatal behavioral development. Depression is a relevant issue and should be addressed as a routine part of prenatal health care.

Prenatal dysthymia versus major depression effects on maternal cortisol and fetal growth

To determine differences between pregnant women diagnosed with Dysthymia versus Major Depression, depressed pregnant women (N=102) were divided by their diagnosis into Dysthymic (N=48) and Major Depression (N=54) groups and compared on self-report measures (depression, anxiety, anger, daily hassles and behavioral inhibition), on stress hormone levels (cortisol and norepinephrine), and on fetal measurements. The Major Depression group had more self-reported symptoms. However, the Dysthymic group had higher prenatal cortisol levels and lower fetal growth measurements (estimated weight, femur length, abdominal circumference) as measured at their first ultrasound (M=18 weeks gestation). Thus, depressed pregnant women with Dysthymia and Major Depression appeared to have different prenatal symptoms.

Prenatal dopamine and neonatal behavior and biochemistry

Depressed pregnant women (N=126) were divided into high and low prenatal maternal dopamine (HVA) groups based on a tertile split on their dopamine levels at 20 weeks gestation. The high versus the low dopamine group had lower Center for Epidemiological Studies-Depression Scale (CES-D) scores, higher norepinephrine levels at the 20-week gestational age visit and higher dopamine and serotonin levels at both the 20- and the 32-week gestational age visits. The neonates of the mothers with high versus low prenatal dopamine levels also had higher dopamine and serotonin levels as well as lower cortisol levels. Finally, the neonates in the high dopamine group had better autonomic stability and excitability scores on the Brazelton Neonatal Behavior Assessment Scale. Thus, prenatal maternal dopamine levels appear to be negatively related to prenatal depression scores and positively related to neonatal dopamine and behavioral regulation, although these effects are confounded by elevated serotonin levels.

Prenatal serotonin and neonatal outcome: brief report

The purpose of the present study was to determine the relationships between prenatal serotonin levels and other biochemical values during pregnancy as well as their relationships to neonatal biochemical and behavioral variables. To address that question, the pregnant women were divided into the top and bottom tertiles based on their serotonin levels at 20 weeks gestational age.