Postpartum Depression: Is it a condition affecting the mother-infant interaction and the development of the child across the first year of life?

Noting that maternal depression is common during a baby's first year, this study examined the interaction of depressed and non-depressed mother-child dyads. A sample of 26 first-time mothers with postpartum depression at the third month after birth and their 3-month-old infants was compared to a sample of 25 first-time mothers with no postpartum depression at the third month after birth and their 3-month-old infants. The observations were repeated at 6 months and again at 12 months postpartum. The samples were compared for differences in mother interaction behavior, mother's infant care, mother's concern with the baby, infant behavioral difficulties, infant mental and motor development, and infant behavior with the observer. Among the findings are the following: (1) depressed mothers' interaction behavior and care of their infants are less adequate than the non-depressed mothers' interaction behavior and care of their infants at 3, 6, and 12 months postpartum; (2) infants' interaction behaviors during feeding and face-to-face interaction with depressed mothers are less adequate than infants' interactions with non-depressed mothers at 3, 6, and 12 months postpartum; (3) mother-infant interactions are less adequate in the depressed mother dyads than the non-depressed dyads at 3, 6, and 12 months postpartum; (4) depressed mothers are less concerned about their infants than non-depressed mothers at 3, 6, and 12 months postpartum; (5) infants of depressed mothers have more behavioral difficulties at 3, 6, and 12 months postpartum than infants of non-depressed mothers; (6) infants of depressed mothers had lower mental and motor development rates at 6 and 12 months postpartum than infants of non-depressed mothers; and (7) infants of non-depressed mothers behaved in a more positive way with the observer than the infants of depressed mothers. (AS)

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.

O efeito da insulina na placentação: estudos in vitro com células HTR8-SVneo

Dissertação de mestrado em Bioquímica Aplicada

Plataforma de apoio à decisão nos cuidados de ginecologia e obstetrícia

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

The alarm distress baby scale in a longitudinal Portuguese study reanalyzed with attachment data

Article first published online: 13 NOV 2013

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.

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.