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

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

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

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

BACKGROUND: Optimal control for the management of the length of stay in the postanesthesia care unit (PACU) following general anesthesia in adults is an important strategy for surgical patients' care. A model to predict the results of the PACU stays could be used to improve the utilization of the PACU and resources of the operating room through a more efficient arrangement. The purpose of this study was to evaluate the performance of the decision tree based analysis using clinical sets of data from adult patients undergoing general anesthesia. METHODS: The decision tree was trained with 351 clinical sets (86% in 409 data sets) using a Chi-squared automatic interaction detection (CHAID) algorithm and validated through independent testing of 58 cases (14%). Twenty-two independent variables were used to find determinant variables and to predict categorical dependent values (lengths of stay in the PACU). RESULTS: The decision tree based analysis correctly predicted in 68% of real situations and identified influencing variables as intubation state, complication in the PACU, and intraoperative transfusion. CONCLUSIONS: We concluded that the decision tree based analysis could provide a useful predictive and classifying model for the optimization of limited resources of the PACU. The decision tree based analysis is an alternative way of classifying, and a predicting method for developing a model for lengths of stay in the PACU with easy interpretation and clear graphical displays of the structure of variables.
Adult ; Anesthesia, General ; Decision Trees ; Humans ; Intubation ; Length of Stay* ; Operating Rooms

OBJECTIVE: This study aimed to use data mining to explore the significantly contributing variables to good social functioning in schizophrenia patients. METHODS: The study cohort comprised 67 schizophrenia patients on stable medication. A total of 51 variables (6 demographic data, 3 illness history, 22 social cognition, 16 neurocognition, 4 psychiatric symptoms) were input into a data-mining decision tree using the Answer Tree program to find the pathway for the best social functioning. RESULTS: Several contributing factors for good social functioning were found. Continuous attention was the strongest contributing factor. Three variables involving best social functioning included good continuous attention, good theory of mind (TOM), and low sensitivity of disgust emotion. CONCLUSION: Our results confirmed the mediating roles of social cognition between neurocognition and functional outcomes, and suggested that social cognition can significantly predict social functioning in schizophrenia patients.
Cognition ; Cohort Studies ; Data Mining ; Decision Trees ; Humans ; Negotiating ; Schizophrenia ; Theory of Mind

This study provides an application of datamining approach to CQJ using the discharge summary. First, we found a process variation in hospital infection rate by SPC (Statistical Process Control) technique. Second, importance of factors influencing hospital infection was inferred through the decision tree analysis which is a classification method in data -mining approach. The most important factor was surgery followed by comorbidity and length of operation. Comorbidity was further divided into age and principal diagnosis and the length of operation was further divided into age and chief complaint. 24 rules of hospital infection were generated by the decision tree analysis. Of these, 9 rules with predictive prover greater than 50% were suggested as guidelines for hospital infection control. The optimum range of target group in hospital infection control were identified through the information gain summary.Association rule, which is another kind of datamining method, was performed to analyze the relationship between principal diagnosis and comorbidity. The confidence score, which measures the degree of association, between urinary tract infection and causal bacillus was the highest, followed by the score between postoperative wound disruption and postoperative wound infection.This study demonstrated how datamining approach could be used to provide information to support prospective surveillance of hospital infection. The datamining technique can also be applied to various areas for CQI using other hospital databases.
Bacillus ; Classification ; Comorbidity ; Cross Infection ; Data Mining* ; Decision Trees ; Diagnosis ; Urinary Tract Infections ; Wounds and Injuries