Maxillary sinus floor augmentation using lateral window and crestal technique is considered as predictable methods to increase the residual bone height; however, this surgery is commonly complicated by Schneiderian membrane perforation, which is closely related to anatomical factors. This article aimed to assess anatomical factors on successful augmentation procedures. After review of the current evidence on sinus augmentation techniques, anatomical factors related to the stretching potential of Schneiderian membrane were assessed and a decision tree for the rational choice of surgical approaches was proposed. Schneiderian membrane perforation might occur when local tension exceeds its stretching potential, which is closely related to anatomical variations of the maxillary sinus. Choice of a surgical approach and clinical outcomes are influenced by the stretching potential of Schneiderian membrane. In addition to the residual bone height, clinicians should also consider the stretching potential affected by the membrane health condition, the contours of the maxillary sinus, and the presence of antral septa when evaluating the choice of surgical approaches and clinical outcomes.
Sinus Floor Augmentation ; Decision Trees

In this paper, we focus on classifying cardiac arrhythmias. The MIT-BIH database is used with 14 original classes of labeling which is then mapped into 5 more general classes, using the Association for the Advancement of Medical Instrumentation standard. Three types of features were selected with a focus on the time-frequency aspects of ECG signal. After using the Wigner-Ville distribution the time-frequency plane is split into 9 windows considering the frequency bandwidth and time duration of ECG segments and peaks. The summation over these windows are employed as pseudo-energy features in classification. The “subject-oriented” scheme is used in classification, meaning the train and test sets include samples from different subjects. The subject-oriented method avoids the possible overfitting issues and guaranties the authenticity of the classification. The overall sensitivity and positive predictivity of classification is 99.67 and 98.92%, respectively, which shows a significant improvement over previous studies.
Arrhythmias, Cardiac* ; Classification* ; Decision Trees ; Electrocardiography* ; Methods

The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients' value.
Decision Support Techniques* ; Decision Trees* ; Evidence-Based Medicine ; Uncertainty

With the development of computer technology, medical data has developed from traditional paper pattern into electronic mode, which could effectively promote the medical development. This paper at first presents the status and characteristics of medical data mining. Then, it discusses the critical method of medical data mining in classification, clustering and prediction, respectively. The paper focuses on the application and assessment of five algorithms which are designed for medical data mining, including decision tree, cluster analysis, association rule, intelligent algorithm and the mix algorithm. Finally, this paper outlooks the data mining application in medical domain.
Algorithms ; Cluster Analysis ; Data Mining ; Decision Trees ; Medical Informatics ; Software

OBJECTIVE: To provide medicare services for patients demands satisfyingly, immediate introduction of the Customer Relationship Management(CRM) is raised inevitable. In this paper we proposed that the minimizing the hospital losses by cut down the rate of cancelation of the hospital reservation, to secure patients as clients. METHODS: And to implement the data mining-based healthcare customer relationship management system applied from the back propagation algorithm of the artificial neural networks technique and the Feature GENeration(FGEN) algorithm of the decision tree technique. RESULTS: In this paper we divided a patient to an appropriate group through a data mining process and classified more correct customer through a campaign process. CONCLUSION: These results would be essential for new patients to enhance hospital reliability, for hospital to select profitable patients with high loyalty and to manage patients efficiently.
Data Mining* ; Decision Trees ; Delivery of Health Care* ; Humans ; Medicare

The purpose of this study was to investigate the use of decision tree for the classification of antimicrobial peptides. The classification was based on the activities of known antimicrobial peptides against common microbes including Escherichia coli and Staphylococcus aureus. A feature selection was employed to select an effective subset of features from available attribute sets.Sequential applications of decision tree with 17 nodes with 9 leaves and 13 nodes with 7 leaves provided the classification rates of 76.74% and 74.66% against E. coli and S. aureus, respectively. Angle subtended by positively charged face and the positive charge commonly gave higher accuracies in both E. coli and S. aureus datasets. In this study, we describe a successful application of decision tree that provides the understanding of the effects of physicochemical characteristics of peptides on bacterial membrane.
Classification* ; Dataset ; Decision Trees* ; Escherichia coli ; Membranes ; Peptides ; Staphylococcus aureus

BACKGROUND: The best options of several bisphosphonates for prevention of osteoporotic fractures in postmenopausal women remain controversial. We determined which bisphosphonate provides better efficacy in prevention of osteoporotic fractures using a decision analysis tool, in terms of quality of life. METHODS: A decision analysis model was constructed containing final outcome score and the probability of vertebral and hip fracture within 1 year. Final outcome was defined as health-related quality of life, and was used as an utility in the decision tree. Probabilities were obtained by literature review, and health-related quality of life was evaluated by consensus committee. A roll back tool was used to determine the best bisphosphonate, and sensitivity analysis was performed to compensate for decision model uncertainty. RESULTS: The decision model favored bisphosphonate with higher compliance in terms of quality of life. In one-way sensitivity analysis, ibandronate was more beneficial than the others, when probability of compliance on ibandronate was above 0.589. CONCLUSIONS: In terms of quality of life, the decision analysis model showed that compliance was most important for patients in real world, regardless of type of bisphosphonate.
Compliance* ; Consensus ; Decision Support Techniques* ; Decision Trees ; Diphosphonates ; Female ; Hip ; Humans ; Osteoporotic Fractures ; Patient Compliance ; Quality of Life ; Uncertainty

In this paper, the Boosting-based decision tree ensemble classifiers were applied to discriminate thermophilic and mesophilic proteins. Three methods, namely, self-consistency test, 5-fold cross-validation and independent testing with other dataset, were used to evaluate the performance and robust of the models. Logitboost, as a novel classifier in Boosting algorithm, performed better than Adaboost. The overall accuracy of the three methods was 100%, 88.4% and 89.5%, respectively. It was demonstrated that LogitBoost performed comparably or even better than that of neural network, a very powerful classifier widely used in biological literatures. The influence of protein size on discrimination was addressed. It is anticipated that the power in predicting many bio-macromolecular attributes will be further strengthened if the Boosting and some other existing algorithms can be effectively complemented with each other.
Algorithms ; Decision Trees ; Molecular Weight ; Neural Networks (Computer) ; Proteins ; chemistry ; classification

OBJECTIVES: This study aimed to identify risk factors and protective factors for the mental health problems of the children of mothers with mental illnesses. METHODS: We interviewed 136 mothers with mental illnesses, each of whom had at least one child under age 18, using a structured questionnaire to obtain sociodemographic data, clinical characteristics (diagnosis, chronicity, number of hospitalizations, symptom severity) and protective factors, such as social support and parental and marital cohesion. In addition, these mothers completed the Korean personality rating scale for their children, evaluating their children's mental health problems (behavioral, emotional, and developmental). RESULTS: High scores on measures of mental health problems in children correlated with significantly lower economic level, fewer social supports, lower marital and parental cohesion and more symptoms associated with the mother's mental illness. Using a decision tree analysis, we determined the important predictors were protective factors (parental cohesion, economic level, and social support), even though risk factors, such as mother's symptoms, were also important. CONCLUSION: This study proposes that for these children, preventive interventions that meet mothers' needs could minimize psychopathology in their children.
Child ; Decision Trees ; Hospitalization ; Humans ; Mental Health ; Mothers ; Parents ; Psychopathology ; Surveys and Questionnaires ; Risk Factors

Proteins are the final executive actor of cell viability and function. Protein-protein interactions determine the complexity of the organism. Research on the protein interactions can help us understand the function of the protein at the molecular level, learn the cell growth, development, differentiation, apoptosis and understand biological regulation mechanisms and other activities. They are essential for understanding the pathologies of diseases and helpful in the prevention and treatment of diseases, as well as in the development of new drugs. In this paper, we employ the single decision-tree classification model to predict protein-protein interactions in the yeast. The original data came from the existing literature. Using software Clementine, this paper analyzes how these attributes affect the accuracy of the model by adjusting the predicted attributes. The result shows that a single decision tree is a good classification model and it has higher accuracy compared to those in the previous researches.
Algorithms ; Decision Trees ; Fungal Proteins ; chemistry ; Models, Theoretical ; Protein Interaction Domains and Motifs ; Protein Interaction Maps