Longterm prediction of failure after treatment of cervical precancer

Non peer reviewe

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

Use of the electrohysterogram signal for characterization of contractions during pregnancy

International audienc

Epidemiologic study in pregnant women from Amiens (Picardy). Need of a national stydy.

Il s'agit d'une enquête réalisée à Amiens du 1er mars 1993 au 28 février 1994 chez les femmes venant d'accoucher pour évaluer leur niveau d'immunité vis à vis de la toxoplasmose et leur connaissances des mesures préventives. L'analyse des données s'est faite grâce au locigiel Epidemio sur un total de 987 femmes. La séroprévalence est de 58% et l'analyse montre que c'est la consommation de viande peu ou pas cuite et la présence d'un chat dans l'entourage qui sont les 2 facteurs de risque majeurs. La quasi totalité des femmes a pu citer au moins 2 moyens de prévention efficaces et les reccommandations sont largement suivies. En conclusion, les résultats sont rassurants. Cependant, une enquête nationale serait nécessaire pour permettre une évaluation sur le plan national

Electrohysterography during pregnancy: preliminary report.

International audienceThe purpose of this study was to test the ability of uterine electrical activity recorded by electrohysterography (EHG) from abdominal electrodes during pregnancy to provide reliable information about uterine contractions. In this preliminary study, abdominal EHG was used to monitor the uterine contractions of eight women, three of whom were having spontaneous contractions related to preterm labor and five of whom were having medical abortions after intrauterine fetal death. The EHG signal consisting of one electrical burst (EB) correlated with a single episode of mechanical activity (MA) in more than 66% of the recorded contractions. When mechanical or electrical activity identified as artifactual was excluded, the temporal correlation of EBs with MA was found in 89% of the recorded contractions. Furthermore, the electrical bursts detected had temporal and spectral characteristics similar to those described previously. Reliable detection of mechanical activity during early pregnancy remains problematic. Nevertheless, abdominal EHG appears suitable for noninvasive monitoring of pregnancies at risk. Further studies are needed to elucidate the significance of the EHG signal in both normal and abnormal pregnancies. It may eventually be possible to use EHG as an ambulatory monitoring tool for the early diagnosis of preterm labor