ABSTRACT
OBJECTIVE: To describe the results of the application of a Machine Learning (ML) model to predict in-hospital cardiac arrests (ICA) 24 hours in advance in the hospital wards. DESIGN: Retrospective observational cohort study. SETTING: Hospital Wards. PATIENTS: Data were extracted from the hospital's Electronic Health Record (EHR). The resulting database contained a total of 750 records corresponding to 620 different patients (370 patients with ICA and 250 control), between may 2009 and december 2021. INTERVENTIONS: No. MAIN VARIABLES OF INTEREST: As predictors of ICA, a set of 28 variables including personal history, vital signs and laboratory data was employed. MODELS: For the early prediction of ICA, predictive models based on the following ML algorithms and using the mentioned variables, were developed and compared: K Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forest, Gradient Boosting and Custom Ensemble of Gradient Boosting estimators (CEGB). EXPERIMENTS: Model training and evaluation was carried out using cross validation. Among metrics of performance, accuracy, specificity, sensitivity and AUC were estimated. RESULTS: The best performance was provided by the CEGB model, which obtained an AUC = 0.90, a specificity = 0.84 and a sensitivity = 0.81. The main variables with influence to predict ICA were level of consciousness, haemoglobin, glucose, urea, blood pressure, heart rate, creatinine, age and hypertension, among others. CONCLUSIONS: The use of ML models could be of great support in the early detection of ICA, as the case of the CEGB model endorsed, which enabled good predictions of ICA.
ABSTRACT
Early intervention during childhood in patients with Autism Spectrum Disorder (ASD) has been strongly advocated. As adolescence is reached, new, more complex social demands emerge. These demands require a therapeutic approach that has not been widely studied. The aim of this review is to examine and synthesize the existing literature on social cognition interventions in adolescence and lay the groundwork for future interventions.
Subject(s)
Autism Spectrum Disorder , Humans , Adolescent , Autism Spectrum Disorder/therapy , Social Cognition , CognitionABSTRACT
Missing data is a common problem in scientific research. The availability of extensive environmental time series is usually laborious and difficult, and sometimes unexpected failures are not detected until samples are processed. Consequently, environmental databases frequently have some gaps with missing data in it. Applying an interpolation method before starting the data analysis can be a good solution in order to complete this missing information. Nevertheless, there are several different approaches whose accuracy should be considered and compared. In this study, data from 6 aerobiological sampling stations were used as an example of environmental data series to assess the accuracy of different interpolation methods. For that, observed daily pollen/spore concentration data series were randomly removed, interpolated by using different methods and then, compared with the observed data to measure the errors produced. Different periods, gap sizes, interpolation methods and bioaerosols were considered in order to check their influence in the interpolation accuracy. The moving mean interpolation method obtained the highest success rate as average. By using this method, a success rate of the 70% was obtained when the risk classes used in the alert systems of the pollen information platforms were taken into account. In general, errors were mostly greater when there were high oscillations in the concentrations of biotic particles during consecutive days. That is the reason why the pre-peak and peak periods showed the highest interpolation errors. The errors were also higher when gaps longer than 5 days were considered. So, for completing long periods of missing data, it would be advisable to test other methodological approaches. A new Variation Index based on the behaviour of the pollen/spore season (measurement of the variability of the concentrations every 2 consecutive days) was elaborated, which allows to estimate the potential error before the interpolation is applied.
Subject(s)
Pollen , Databases, Factual , SeasonsABSTRACT
INTRODUCTION: Neurophysiological studies conducted in subjects who it is suspected are suffering from Creutzfeldt-Jakob disease (CJD) are usually aimed at searching for what is called a typical EEG. However, the EEG is a dynamic test and therefore subject to variations in time. Furthermore, there are other kinds of neurophysiological tests that may also be of interest, and the lack of typical traces in the new variant of the disease forces us to explore other diagnostic approaches. METHOD: We performed a clinical EEG correlation in the course of the evolution of the disease, in which we observed a significant variability throughout the different stages. We then review the neurophysiological studies that have been conducted on CJD, in which shortcomings and important discrepancies can be seen. CONCLUSION: EEG has proved to be a fundamental element in CJD probability diagnosis. It is also found that sleep and waking records, obtaining series of EEGs, the detection of poligraphic changes related with variations in the degree of consciousness and, lastly, studies conducted by means of other neurophysiological techniques associated with clinical data will all undoubtedly enable us to achieve higher efficiency in diagnosis