Aarupmunksgaard1699

lderly females with ovarian cancer. Additionally, machine learning effectively predicted LOS. SUMMARY  The current study sought to determine whether elements of nursing narratives could be used to predict postoperative LOS among elderly ovarian cancer patients. Results indicated that nursing narratives that used the words "urination," "food supply," "bowel mobility," and "pain" significantly predicted postoperative LOS in the study population. selleck kinase inhibitor Additionally, it was found that machine learning could effectively predict LOS based on quantitative characteristics of nursing narratives. Georg Thieme Verlag KG Stuttgart · New York.OBJECTIVES  This article aims to determine possible improvements made by feature extraction methods to the machine learning prediction methods for predicting 30-day hospital readmissions. METHODS  The study evaluates five feature extraction methods including principal component analysis (PCA), kernel principal component analysis (KPCA), isomap, Laplacian eigenmaps, and locality preserving projections (LPPs) for improving the accuracy of nine machine learning prediction methods in predicting 30-day hospital readmissions. The specific prediction methods considered include logistic regression, Cox regression, linear discriminant analysis, k-nearest neighbor (KNN), support vector machines (SVMs), bagged trees, boosted trees, random forest, and artificial neural networks. All models are developed in MATLAB and validated using area under the curve based on two population-based data sets from partner hospitals. RESULTS  Laplacian eigenmaps and isomap feature extraction provide the most improvement to the readmission predictive accuracy of KNN, SVM, bagged trees, boosted trees, and linear discriminant analysis methods. The results for artificial neural networks, random forest, Cox regression, and logistic regression show improvement for only one of the data sets. Also, PCA and LPP provided the best computation efficiency followed by KPCA, Laplacian eigenmaps, and isomap. CONCLUSION  Feature extraction methods can improve the predictive performance of machine learning methods for predicting readmissions. However, the improvement depended on the specific choice of the prediction method, feature extraction method, and the complexity of the data set features. Georg Thieme Verlag KG Stuttgart · New York.BACKGROUND  The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged. OBJECTIVE  This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used. METHODS  A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification algorithms in the process of predicting of aGvHD were considered. RESULTS  After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables. CONCLUSION  Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly. Georg Thieme Verlag KG Stuttgart · New York.BACKGROUND  Development of ontologies in traditional medicine can be a foundation for other applications of informatics in this field. Despite the importance of the development of ontologies in traditional medicine, there are few review studies in this area. This study aims to review different methods for ontology development and evaluation in traditional medicine. METHODS  This review study was performed in 2019. To find related papers, six databases including Scopus, Web of Science, PubMed, Embase, IEEE Xplore, and SpringerLink were searched. Initially, 761 articles were identified. After applying inclusion and exclusion criteria, 22 articles were selected to review different methods for ontology development and evaluation in traditional medicine. RESULTS  Five different methods were used for ontology development in traditional medicine, namely conventional, customized, semiautomatic, upper-level, and large-scale methods. The results showed that ontology evaluation was only considered in 32% of the studies. The common methods used for ontology evaluation were competency questions, expert-based evaluation, and automatic detection of inconsistency errors. CONCLUSION  Development of ontologies is of high importance for organizing knowledge in traditional medicine, as this branch of medicine is often not documented or is documented in local languages. The results of this study can help ontology developers to be familiar with the common methods of ontology development and evaluation in traditional medicine and use them for future research. Georg Thieme Verlag KG Stuttgart · New York.BACKGROUND  Health dialog systems have seen increased adoption by patients, hospitals, and universities due to the confluence of advancements in machine learning and the ubiquity of high-performance hardware that supports real-time speech recognition, high-fidelity text-to-speech, and semantic understanding of natural language. OBJECTIVES  This review seeks to enumerate opportunities to apply dialog systems toward the improvement of health outcomes while identifying both gaps in the current literature that may impede their implementation and recommendations that may improve their success in medical practice. METHODS  A search over PubMed and the ACM Digital Library was conducted on September 12, 2017 to collect all articles related to dialog systems within the domain of health care. These results were screened for eligibility with the main criteria being a peer-reviewed study of a system that includes both a natural language interface and either end-user testing or practical implementation. RESULTS  Forty-six studies met the inclusion criteria including 24 quasi-experimental studies, 16 randomized control trials, 2 case-control studies, 2 prospective cohort studies, 1 system description, and 1 human-computer conversation analysis.