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

Humans ; Cost-Benefit Analysis ; HIV Infections/drug therapy* ; Cost-Effectiveness Analysis ; Decision Trees

Objective: To investigate the influencing factors of HBV intrauterine transmission and their interaction effects by integrating logistic regression model and Chi-squared automatic interaction detector (CHAID) decision tree model. Methods: A total of 689 pairs of HBsAg-positive mothers and their neonates in the obstetrics department of the Third People's Hospital of Taiyuan from 2007 to 2013 were enrolled, and the basic information of mothers and their neonates were obtained by questionnaire survey and medical record review, such as the general demographic characteristics, gestational week and delivery mode. HBV DNA and HBV serological markers of the mothers and newborns were detected by fluorescence quantitative PCR and electrochemiluminescence immunoassay respectively. The CHAID decision tree model and unconditional logistic regression analysis were used to explore the factors influencing HBV intrauterine transmission in neonates of HBsAg-positive mothers. Results: Among the 689 neonates, the incidence of HBV intrauterine transmission was 11.47% (79/689). After adjusted for confounding factors, the first and second logistic multivariate analysis showed that cesarean delivery was a protective factor for HBV intrauterine transmission (OR=0.25, 95%CI: 0.14-0.43; OR=0.27, 95%CI: 0.15-0.46); both models indicated that maternal HBeAg positivity and HBV DNA load ≥2×10⁵ IU/ml before delivery were risk factors of HBV intrauterine transmission (OR=3.89, 95%CI: 2.32-6.51; OR=3.48, 95%CI: 2.12-5.71), respectively. The CHAID decision tree model screened three significant factors influencing HBV intrauterine transmission, the most significant one was maternal HBeAg status, followed by delivery mode and maternal HBV DNA load. There were interactions between maternal HBeAg status and delivery modes, as well as delivery mode and maternal HBV DNA load before delivery. The rate of HBV intrauterine transmission in newborns of HBeAg-positive mothers by vaginal delivery increased from 19.08% to 29.37%; among HBeAg-positive mothers with HBV DNA ≥2×10⁵ IU/ml, the rate of HBV intrauterine transmission increased to 33.33% in the newborns by vaginal delivery. Conclusions: Maternal HBeAg positivity,maternal HBV DNA ≥2×10⁵ IU/ml and vaginal delivery could be risk factors for HBV intrauterine transmission in newborns. Interaction effects were found between maternal HBeAg positivity and vaginal delivery, as well as vaginal delivery and high maternal HBV DNA load. Logistic regression model and the CHAID decision tree model can be used in conjunction to identify the high-risk populations and develop preventive strategies accurately.
DNA, Viral/genetics* ; Decision Trees ; Female ; Hepatitis B Surface Antigens ; Hepatitis B e Antigens ; Hepatitis B virus/genetics* ; Humans ; Infant, Newborn ; Infectious Disease Transmission, Vertical ; Logistic Models ; Mothers ; Pregnancy ; Pregnancy Complications, Infectious/epidemiology*

OBJECTIVES: To study the clinical value of attention time combined with behavior scale in the screening of attention deficit hyperactivity disorder (ADHD) in preschool children. METHODS: A total of 200 preschool children with ADHD diagnosed in Fujian Maternal and Child Health Hospital from February 2019 to March 2020 were enrolled as the ADHD group. A total of 200 children who underwent physical examination in the hospital or kindergartens during the same period were enrolled as the control group. Attention time was recorded. Chinese Version of Swanson Nolan and Pelham, Version IV Scale-Parent Form (SNAP-IV) scale was used to evaluate symptoms. With clinical diagnosis as the gold standard, the decision tree analysis was used to evaluate the clinical value of attention time combined with behavior scale in the screening of ADHD. RESULTS: Compared with the control group, the ADHD group had significantly higher scores of SNAP-IV items 1, 4, 7, 8, 10, 11, 14, 15, 16, 18, 20, 21, and 22 (P<0.05) and a significantly shorter attention time (P<0.05). The variables with statistically significant differences between the two groups in univariate analysis were used as independent variables to establish a decision tree model. The accuracy of the model in predicting ADHD was 81%, that in predicting non-ADHD was 69%, and the overall accuracy was 75%, with an area under the ROC curve of 0.816 (95% CI: 0.774-0.857, P<0.001). CONCLUSIONS The decision tree model for screening ADHD in preschool children based on attention time and assessment results of behavior scale has a high accuracy and can be used for rapid screening of ADHD among children in clinical practice.
Asians ; Attention Deficit Disorder with Hyperactivity/diagnosis* ; Child, Preschool ; Decision Trees ; Humans ; Mass Screening ; Prospective Studies

Adolescent ; Adult ; Child ; Decision Trees ; Female ; HIV Infections ; economics ; transmission ; Humans ; Infectious Disease Transmission, Vertical ; economics ; prevention & control ; Kenya ; Middle Aged ; Mothers ; South Africa ; Vietnam ; Young Adult

PURPOSE: The aim of this study is to explore levels of suicidal ideation and identify subgroups of high suicidal risk among the depressed elderly in Korea. METHODS: A descriptive cross-sectional design was adopted on secondary data from the 6th (1st year) Korean national health and nutrition examination survey (KNHANES). A total of 239 depressed elders aged 60 or over who participated in the KNHANES. The prevalence of suicidal ideation and its related factors, including sociodemographic, physical, psychological characteristics and quality of life (EQ-5D index) were examined. Descriptive statistics and a decision tree analysis were performed using the SPSS/WIN 23.0 and SPSS Modeler 14.2 programs. RESULTS: Of the depressed elderly, 28.9% had suicidal ideation. Three groups with high suicidal ideation were identified. Predictive factors included perceived stress level, household income level, quality of life and restriction of activity. In the highest risk group were those depressed elderly with moderate and low levels of stress, less than .71 of EQ-5D index and restriction of activity, and 80.0% of these participants had suicidal ideation. The accuracy of the model was 80.8%, its sensitivity 85.9%, and its specificity 68.1%. CONCLUSION: Multi-dimensional intervention should be designed to decrease suicide among the depressed elderly, particularly focusing on subgroups with high risk factors. This research is expected to contribute itself to the policy design and solution building in the future as it suggests policy implications in preventing the suicide of the depressed elderly.
Aged ; Decision Trees ; Depression ; Family Characteristics ; Humans ; Korea ; Nutrition Surveys ; Prevalence ; Quality of Life ; Risk Factors ; Sensitivity and Specificity ; Suicidal Ideation ; Suicide

PURPOSE: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. METHODS: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. RESULTS: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. CONCLUSION: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.
Case-Control Studies ; Data Mining ; Decision Support Systems, Clinical ; Decision Trees ; Electronic Health Records ; Hospitals, Teaching ; Humans ; Incidence ; Korea ; Logistic Models ; Patient Safety ; Pressure Ulcer ; Proportional Hazards Models ; Retrospective Studies ; ROC Curve ; Ulcer

BACKGROUND: As Koreans adopt more Westernized diets, consumer demands for processed food products are growing. The Korean government implemented a food labeling system to help people reasonably choose processed foods. This study investigated the utilization of these food labels among Koreans, as well as demographic differences between users and non-users of food labels, ultimately presenting foundational data for strategies to enhance dietary lifestyles by facilitating the use of food labeling. METHODS: Data from the 2014–2017 Community Health Survey were used. The Cochran-Armitage trend test was performed to investigate whether food labeling awareness and utilization increase over time; a data mining technique called decision tree analysis was then used to examine the effects of sociodemographic factors on the use of food labeling for each of the 4 years. RESULTS: Food labeling awareness increased by 4.5% over 4 years; the Cochran–Armitage trend test showed that this increase was statistically significant (χ2 = 1,068.21; P < 0.001). Food labeling utilization increased significantly but marginally, by 0.3%, over 4 years (χ2 = 20.154; P < 0.001). Decision tree analysis showed that food labeling users comprised mainly women in their 30s–50s currently in graduate school or with a graduate degree; the non-user group comprised mainly divorced/widowed/separated people in their 70s who were elementary school grads or below. CONCLUSION: Education level, which could be related to health literacy, substantially affected the use of food labeling. Education level is a typical socioeconomic index, and if people who are less educated tend not to use food labeling, it can lead to health inequity. Thus, food labeling should be reformed with consideration of health literacy among the less-educated social classes. From a health communication perspective, images and pictures can boost consumers' understanding, which should be considered to improve comprehensibility of food labeling.
Data Mining ; Decision Trees ; Diet ; Education ; Female ; Food Labeling ; Health Communication ; Health Literacy ; Health Surveys ; Humans ; Korea ; Life Style ; Social Class

BACKGROUND: Osteomyelitis is an intraosseous inflammatory disease characterized by progressive inflammatory osteoclasia and ossification. The use of quantitative analysis to assist interpretation of osteomyelitis is increasingly being considered. The objective of this study was to perform early diagnosis of osteomyelitis on digital panoramic radiographs using basic functions provided by picture archiving and communication system (PACS), a program used to show radiographic images. METHODS: This study targeted a total of 95 patients whose symptoms were confirmed as osteomyelitis under clinical, radiologic, pathological diagnosis over 11 years from 2008 to 2017. Five categorized patients were osteoradionecrosis, bisphosphonate-related osteonecrosis of jaw (BRONJ, suppurative and sclerosing type), and bacterial osteomyelitis (suppurative and sclerosing type), and the control group was 117 randomly sampled. The photographic density in a certain area of the digital panoramic radiograph was determined and compared using the “measure area rectangle,” one of the basic PACS functions in INFINITT PACS® (INFINITT Healthcare, Seoul, South Korea). A conditional inference tree, one type of decision making tree, was generated with the program R for statistical analysis with SPSS®. RESULTS: In the conditional inference tree generated from the obtained data, cases where the difference in average value exceeded 54.49 and the difference in minimum value was less than 54.49 and greater than 12.81 and the difference in minimum value exceeded 39 were considered suspicious of osteomyelitis. From these results, the disease could be correctly classified with a probability of 88.1%. There was no difference in photographic density value of BRONJ and bacterial osteomyelitis; therefore, it was not possible to classify BRONJ and bacterial osteomyelitis by quantitative analysis of panoramic radiographs based on existing research. CONCLUSIONS: This study demonstrates that it is feasible to measure photographic density using a basic function in PACS and apply the data to assist in the diagnosis of osteomyelitis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40902-019-0188-2) contains supplementary material, which is available to authorized users.
Decision Making ; Delivery of Health Care ; Diagnosis ; Early Diagnosis ; Humans ; Jaw ; Osteomyelitis ; Osteonecrosis ; Osteoradionecrosis ; Seoul ; Trees

Scratching is a main behavioral response accompanied by acute and chronic itch conditions, and has been quantified as an objective correlate to assess itch in studies using laboratory animals. Scratching has been counted mostly by human annotators, which is a time-consuming and laborious process. It has been attempted to develop automated scoring methods using various strategies, but they often require specialized equipment, costly software, or implantation of device which may disturb animal behaviors. To complement limitations of those methods, we have adapted machine learning-based strategy to develop a novel automated and real-time method detecting mouse scratching from experimental movies captured using monochrome cameras such as a webcam. Scratching is identified by characteristic changes in pixels, body position, and body size by frame as well as the size of body. To build a training model, a novel two-step J48 decision tree-inducing algorithm along with a C4.5 post-pruning algorithm was applied to three 30-min video recordings in which a mouse exhibits scratching following an intradermal injection of a pruritogen, and the resultant frames were then used for the next round of training. The trained method exhibited, on average, a sensitivity and specificity of 95.19% and 92.96%, respectively, in a performance test with five new recordings. This result suggests that it can be used as a non-invasive, automated and objective tool to measure mouse scratching from video recordings captured in general experimental settings, permitting rapid and accurate analysis of scratching for preclinical studies and high throughput drug screening.
Animals ; Animals, Laboratory ; Behavior, Animal ; Body Size ; Complement System Proteins ; Decision Trees ; Drug Evaluation, Preclinical ; Humans ; Injections, Intradermal ; Machine Learning ; Methods ; Mice ; Motion Pictures as Topic ; Pruritus ; Research Design ; Sensitivity and Specificity ; Video Recording