Background Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians’ ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. Methods This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes. Results 17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models. Conclusion Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs.
Introduction:This study aims at developing a fuzzy expert system to predict the possibility of neonatal death.Materials and Methods:A questionnaire was given to Iranian neonatologists and the more important factors were identified based on their answers. Then, a computing model was designed considering the fuzziness of variables having the highest neonatal mortality risk. The inference engine used was Mamdani’s method and the output was the risk of neonatal death given as a percentage. To validate the designed system, neonates’ medical records real data at a Tehran hospital were used. MATLAB software was applied to build the model, and user interface was developed by C# programming in Visual Studio platform as bilingual (English and Farsi user interface).Results:According to the results, the accuracy, sensitivity, and specificity of the model were 90%, 83% and 97%, respectively.Conclusion:The designed fuzzy expert system for neonatal death prediction showed good accuracy as well as proper specificity, and could be utilized in general hospitals as a clinical decision support tool.
Introduction: Hepatitis C virus is the leading cause of mortality from liver disease. Also, diagnosis systems are usable tools for better disease control and management. The aim of this study was to design an HCV disease prediction system and classify its severity based on data mining methods. Method: This is an applied research that uses the hepatitis C dataset in the UCI library. The study was conducted in four steps including data preprocessing, data mining, evaluation and system design. In data pre-processing, data balancing techniques were performed. Then, three data mining algorithms (Multi-Layer Perceptron, Bayesian network, and decision tree) were implemented and 10-fold cross-validation method was used to evaluate data mining algorithms. Finally, user interface was designed in MATLAB programming language (version 2016) based on the best algorithm.Results:The results showed that the over-sampling method improved the performance measures of data mining algorithms in disease prediction, so that in the O-dataset the accuracy of the best method (random forest) was 99.9%. Also, the random forest for the O-dataset had the best performance measures in term of sensitivity, accuracy and f-measure (99.9%) and the 100% specificity amount.Conclusion: Considering that the presented approach has performed better than all suggested methods in previous studies, the proposed system in this study can be used well in HCV diagnosing and determining its severity.
BackgroundPremature birth is a global epidemic of significant public health concern. Counselling and education of pregnant women at risk of preterm birth or mothers with premature infants are essential to improve mother and infant health. Mobile applications are an increasingly popular tool among parents to receive health information and education. This study aims to evaluate the usages and the effects of a mobile application designed for premature births in order to improve health outcomes.MethodsThis review will include all studies of different designs which evaluated the use and impact of interventions provided via mobile applications on pregnant women at risk of preterm birth or mothers with premature infants in order to address all health outcomes. A combination of keywords and MeSH(Medical Subject Headings) terms is used in the search strategy. Literature databases including Scopus, PubMed, ISI Web of Science, ProQuest, CINAHL and Cochrane Library will be searched to May 2021. Furthermore, eligible studies will be chosen from the reference list of retrieved papers. Two researchers will independently review the retrieved citations to decide whether they meet the inclusion criteria. Mixed Methods Appraisal Tool (MMAT) V.2018 will be used to assess the quality of studies. Relevant data are collected in a data extraction form and analysed. Results are reported under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.DiscussionThis systematic review will recognize and combine evidence about the usages and impact of mobile application interventions on the health improvement of pregnant women at risk of preterm birth or mothers with premature infants.
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