Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.
For the current engineering degrees, it is necessary to design a monitoring process in order to supervise the subject called "Final Degree Project." This method must be able to manage and evaluate the process of completing the project and submitting the documentation. This paper describes the design of an adaptive method and how to put this mechanism into practice using the learning management system Moodle. Adaptivity provides the chance to study different scenarios that can be produced in cooperation with students and their tutors. The conclusions of this paper show a high level of satisfaction with the adaptive method used in the subject.
In the academic context teamwork has a dual mission: to train students in teamwork competence and the active participation of students in their own learning. Authentic leadership of teams is the key to both goals. This paper presents a research which relates leadership, team grades (individual and group) and student-student interactions. The CTMTC teamwork method is used, as it allows continuous monitoring of teamwork and evaluates the work of the leader and the rest of the team members separately. The measurement tools, a survey for the individual opinion on the authentic leader actions, and a learning analytics system to analyze student-student interactions in forums, help to confirm the following hypothesis: that CTMTC encourages leadership role, that leadership skills are related with team grades and that learning analytics systems help predicting the behavior of teams with true leadership.
Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections.
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