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Validity of neural network in sleep apnea

SLEEP 1999年第1期22卷 105-111页

作者： El-Solh, AA Mador, MJ Ten-Brock, E Shucard, DW Abul-Khoudoud, M Grant, BJB Dept Vet Affairs Med Ctr Buffalo NY 14215 USA SUNY Buffalo Buffalo Gen Hosp Sch Med & Biomed Sci Sleep CtrDept Med Buffalo NY USA SUNY Buffalo Buffalo Gen Hosp Sch Med & Biomed Sci Sleep CtrDept Neurol Buffalo NY USA SUNY Buffalo Buffalo Gen Hosp Sch Med & Biomed Sci Sleep CtrCtr Sleep Disorders Res Buffalo NY USA

Clinical assessment of obstructive sleep apnea (OSA) is poor. Overnight polysomnography (OPG) is the standard reference test, but ii is expensive and time-consuming. We developed an artificial neural network (ANN] using anthropomorphic measurements and clinical information to predict the apnea-hypopnea index (AHI). All patients completed a questionnaire about steep symptoms, sleep behavior, and demographic information prior to undergoing OPG. Neck circumference, height, and weight were obtained on presentation to the sleep center. Twelve variables were used as inputs. The output was an estimate of the AHI. The network was trained with a back-propagation algorithm on 189 patients and validated prospectively on 80 additional patients. Data from the derivation group was used to calculate the 95% confidence interval of the estimated AHI. Predictive accuracy at different AHI thresholds was assessed by the c-index, which is equivalent to the area under the receiver operator characteristic curve. The c-index for predicting OSA in the validation set was 0.96 +/- 0.0191 SE, 0.951 +/- 0.0203 SE, and 0.935 +/- 0.0274 SE, using thresholds of >10, >15, and >20/hour respectively. The actual AHI of the 80 patients in the validation data set fell within the 95% confidence limits of the values predicted by the ANN. This study suggests that ANN may be useful as a predictive tool for OSA.

关键词： artificial neural network classification and regression tree sleep apnea apnea hypopnea index c-index

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Differential impact of age on survival in head and neck cancer according to classic Cox regression and decision tree analysis

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CLINICAL OTOLARYNGOLOGY 2019年第3期44卷 244-253页

作者： Yang, Ching-Chieh Su, Yu-Chieh Lin, Yu-Wei Huang, Chung-, I Lee, Ching-Chih Chi Mei Med Ctr Dept Radiat Oncol Tainan Taiwan Natl Sun Yat Sen Univ Inst Biomed Sci Kaohsiung Taiwan Chia Nan Univ Pharm & Sci Dept Pharm Tainan Taiwan Kaohsiung Med Univ Hosp Dept Internal Med Div Hematol & Oncol Kaohsiung Taiwan Kaohsiung Med Univ Coll Med Fac Med Kaohsiung Taiwan E Da Canc Hosp Dept Radiat Oncol Kaohsiung Taiwan Kaohsiung Vet Gen Hosp Dept Otolaryngol Head & Neck Surg Kaohsiung Taiwan Natl Yang Ming Univ Inst Hosp & Hlth Care Adm Taipei Taiwan Natl Yang Ming Univ Sch Med Taipei Taiwan Triserv Gen Hosp Dept Otolaryngol Head & Neck Surg Taipei Taiwan

ObjectivesTo assess the impact of age on the survival of patients with head and neck squamous cell carcinoma (HNSCC) using different statistical methods. DesignA retrospective population-based study. SettingSurveillance, Epidemiology, and End Results database. Subjects and methodsA total of 28639 patients with newly diagnosed HNSCC were enrolled between 1 January 2007 and 31 December 2013. The effect of age on 5-year disease-specific survival was calculated using a Kaplan-Meier method and compared using log-rank tests. A Cox proportional hazards model was used for a multivariate analysis. A classification and regression tree (CART) analysis that partitioned patients with significantly different Kaplan-Meier curves was introduced to identify the important cancer-related parameters influencing survival. ResultsUni- and multivariate analyses indicated that patients who were older than 60years had poorer 5-year disease-specific survival regardless of tumour subsite and tumor-node-metastasis (TNM) stage. However, the CART analysis determined that age played only a minor role in survival after comparing with other prognosticators. The relative importance of age using the Gini index was as follows: 3.21% for oral cancer, 8.32% for oropharyngeal cancer, 2.56% for hypopharyngeal cancer and 16.51% for laryngeal cancer. ConclusionsDifferent to traditional statistical methods, the CART analysis which was used to identify homogeneous populations revealed that the impact of age varied for different patient groups according to the presence or absence of other prognosticators. This important information could help to guide our clinical decisions and future researches.

关键词： age classification and regression tree head and neck squamous cell carcinoma regression analysis survival

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classification trees for identifying non-use of community-based long-term care services among older adults

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HEALTH POLICY 2017年第10期121卷 1093-1099页

作者： Penkunas, Michael James Eom, Kirsten Yuna Chan, Angelique Wei-Ming Clinton Hlth Access Initiat Inc Boston MA USA Duke NUS Med Sch Ctr Ageing Res & Educ Singapore Singapore Duke NUS Med Sch Program Hlth Serv & Syst Res 8 Coll Rd Singapore 169857 Singapore Natl Univ Singapore Dept Sociol Singapore Singapore

Home- and center-based long-term care (LTC) services allow older adults to remain in the community while simultaneously helping caregivers cope with the stresses associated with providing care. Despite these benefits, the uptake of community-based LTC services among older adults remains low. We analyzed data from a longitudinal study in Singapore to identify the characteristics of individuals with referrals to home-based LTC services or day rehabilitation services at the time of hospital discharge. classification and regression tree analysis was employed to identify combinations of clinical and sociodemographic characteristics of patients and their caregivers for individuals who did not take up their referred services. Patients' level of limitation in activities of daily living (ADL) and caregivers' ethnicity and educational level were the most distinguishing characteristics for identifying older adults who failed to take up their referred home-based services. For day rehabilitation services, patients' level of ADL limitation, home size, age, and possession of a national medical savings account, as well as caregivers' education level, and gender were significant factors influencing service uptake. Identifying subgroups of patients with high rates of non-use can help clinicians target individuals who are need of community-based LTC services but unlikely to engage in formal treatment. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Community-based long-term care classification and regression tree Singapore

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Model Building to Investigate the Role of Spatial Location in Classifying Satellite Image Using SVM, CART and mBACT: A Case Study

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2017年第4期45卷 569-578页

作者： Agarwal, Reshu Acad Univ Dept Math & Stat Wolfville NS Canada

This paper investigates the importance of spatial location of pixels in terms of row-column as an additional explanatory variable in classification along with available spectral bands of remotely sensed data. In view of this, a forward step-wise variable selection algorithm is used to select significant bands/variables and build an optimal model to extract the maximum accuracy. Author performed a case study on the area of town of Wolfville acquired by LANDSAT 5 TM data containing six 30 m resolution spectral bands and pixel location as an additional variable. Data are classified into seven classes using three advanced classifiers i.e. classification and regression trees (CART), support vector machines (SVM) and multi-class Bayesian additive classification tree (mBACT). Traditionally, it is assumed that addition of more explanatory variables always increase the accuracy of classified satellite images. However, results of this study show that adding more variables may sometimes confuse the classifier, that is, if selected carefully, fewer variables can provide the more accurate classification. Importance of row-column information turns out to be more beneficial for mBACT followed by SVM. Interestingly, spatial locations did not turn out to be useful for CART. Based on the findings of this study, mBACT appears to be a slightly better classifier than SVM and a substantially better than CART.

关键词： classification and regression tree Support vector machine LANDSAT data mBACT

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Novel Prognostic Models for Predicting the 180-day Outcome for Patients with Hepatitis-B Virus-related Acute-on-chronic Liver Failure

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Journal of Clinical and Translational Hepatology 2021年第4期9卷 514-520页

作者： Ran Xue Jun Yang Jing Wu Zhongying Wang Qinghua Meng Department of Medical Oncology Beijing You’an HospitalCapital Medical UniversityBeijingChina Key Laboratory of Carcinogenesis&Translational Research(Ministry of Education/Beijing) Early Drug Development CenterPeking University Cancer Hospital&InstituteBeijingChina Department of Integrated Traditional and Western Liver Disease Beijing You’an HospitalCapital Medical UniversityBeijingChina Department of Infection Center Beijing You’an HospitalCapital Medical UniversityBeijingChina

Background and Aims:It remains difficult to forecast the 180-day prognosis of patients with hepatitis B virus-acuteon-chronic liver failure(HBV-ACLF)using existing prognostic *** present study aimed to derive novel-innovative models to enhance the predictive effectiveness of the 180-day mortality in ***:The present cohort study examined 171 HBV-ACLF patients(non-survivors,n=62;survivors,n=109).The 27 retrospectively collected parameters included the basic demographic characteristics,clinical comorbidities,and laboratory *** stepwise logistic regression(LR)and the classification and regression tree(CART)analysis were used to derive two predictive ***,a nomogram was created based on the LR *** accuracy of the LR and CART model was detected through the area under the receiver operating characteristic curve(AUROC),compared with model of end-stage liver disease(MELD)***:Among 171 HBV-ACLF patients,the mean age was 45.17 years-old,and 11.7%of the patients were *** LR model was constructed with six independent factors,which included age,total bilirubin,prothrombin activity,lymphocytes,monocytes and hepatic *** following seven variables were the prognostic factors for HBV-ACLF in the CART model:age,total bilirubin,prothrombin time,lymphocytes,neutrophils,monocytes,and blood urea *** AUROC for the CART model(0.878)was similar to that for the LR model(0.878,p=0.898),and this exceeded that for the MELD scores(0.728,p<0.0001).Conclusions:The LR and CART model are both superior to the MELD scores in predicting the 180-day mortality of patients with *** the LR and CART model can be used as medical decision-making tools by clinicians.

关键词： classification and regression tree Acute-on-chronic hepatitis B liver failure MELD scores Logistic regression model

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Should Sentinel Lymph Node Biopsy Be Performed for All T1b Melanomas in the New 8th Edition American Joint Committee on Cancer Staging System?

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JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS 2019年第4期228卷 466-472页

作者： Egger, Michael E. Stevenson, Megan Bhutiani, Neal Jordan, Adrienne C. Scoggins, Charles R. Philips, Prejesh Martin, Robert C. G., II McMasters, Kelly M. Univ Louisville Dept Surg 315 E BroadwayM-10 Louisville KY 40202 USA Univ Louisville Dept Pathol & Lab Med Louisville KY 40292 USA

BACKGROUND: In the 8th edition of the American Joint Committee on Cancer melanoma staging system, the T1b category has been redefined based solely on thickness and ulceration. National Comprehensive Cancer Network guidelines recommend consideration of sentinel lymph node biopsy (SLNB) for all patients with T1b melanomas (0.8 to 1.0 mm thick). We hypothesized that the new staging system would lead to excessive use of SLNB in patients with non-ulcerated T1b melanomas with a low risk of positive sentinel lymph nodes. STUDY DESIGN: The National Cancer Database 2015 Melanoma Public Use File was used to select patients undergoing SLNB for thin T1 cutaneous melanoma from 2010 to 2015. Clinicopathologic risk factors for having a positive SLNB were evaluated. Univariable and multivariable logistic regression models and classification and regression tree analysis were performed to identify groups with high and low risk of positive SLNB. RESULTS: We selected patients undergoing SLNB without ulceration with thickness 0.75 to 1.04 mm, staged T1b in the new 8th edition American Joint Committee on Cancer by thickness criteria alone (6,894 patients). Independent risk factors for a positive sentinel lymph node were age 56 years or younger (odds ratio [OR] 1.74;95% CI 1.38 to 2.17), thickness 1.0 vs 0.8 to 0.9mm(OR 1.36;95% CI 1.09 to 1.70), female sex (OR1.36;95% CI 1.09 to 1.69), and mitotic rate >= 1/mm(2) (OR 2.01;95% CI 1.54 to 2.64). classification and regression tree analysis identified 2 groups based on age, mitotic rate, and thickness with a risk of positive SLNB <5%. These 2 groups made up 55% of T1b, nonulcerated melanoma patients who underwent SLNB. CONCLUSIONS: The new 8th edition American Joint Committee on Cancer melanoma staging system T1b category should not be used to determine use of SLNB in thin melanoma, as more than one half of T1b lesions without ulceration have a low risk of positive sentinel lymph nodes. (C) 2019 by the American College of Surgeons. Published

关键词： AJCC American Joint Committee on Cancer CART classification and regression tree MR mitotic rate NCDB National Cancer Database SLN sentinel lymph node

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Geographical Disparity and Associated Factors of COPD Prevalence in China: A Spatial Analysis of National Cross-Sectional Study

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INTERNATIONAL JOURNAL OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE 2020年 15卷 367-377页

作者： Wang, Ning Cong, Shu Fan, Jing Bao, Heling Wang, Baohua Yang, Ting Feng, Yajing Liu, Yang Wang, Linhong Wang, Chen Hu, Wenbiao Fang, Liwen Chinese Ctr Dis Control & Prevent Natl Ctr Chron Noncommunicable Dis Control & Prev 27 Nanwei Rd Beijing 100050 Peoples R China Queensland Univ Technol Inst Hlth & Biomed Innovat Sch Publ Hlth & Social Work Brisbane Qld 4059 Australia China Japan Friendship Hosp Ctr Resp Med Beijing Peoples R China Emory Univ Rollins Sch Publ Hlth Dept Environm Hlth Atlanta GA 30322 USA Chinese Acad Med Sci & Peking Union Med Coll Beijing Peoples R China

Purpose: COPD prevalence has rapidly increased in China, but the geographical disparities in COPD prevalence remain largely unknown. This study aimed to assess city-level disparities in COPD prevalence and identify the relative importance of COPD related risk factors in mainland China. Patients and Methods: A nationwide cross-sectional study of COPD recruited 66,752 adults across the mainland China between 2014 and 2015. Patients with COPD were ascertained by a post-bronchodilator pulmonary function test. We estimated the city-specific prevalence of COPD by spatial kriging interpolation method. We detected spatial clusters with a significantly higher prevalence of COPD by spatial scan statistics. We determined the relative importance of COPD associated risk factors by a nonparametric and nonlinear classification and regression tree (CART) model. Results: The three spatial clusters with the highest prevalence of COPD were located in parts of Sichuan, Gansu, and Shaanxi, etc. (relative risks (RRs)) ranging from 1.55 (95% CI 1.55-1.56) to 1.33 (95% CI 1.33-1.33)). CART showed that advanced age (>= 60 years) was the most important factor associated with COPD in the overall population, followed by smoking. We estimated that there were about 28.5 million potentially avoidable cases of COPD among people aged 40 or older if they never smoked. PM2.5 was an important associated risk factor for COPD in the north, northeast, and southwest of China. After adjusting for age and smoking, the spatial cluster with the highest prevalence shifted to most of Sichuan, Gansu, Qinghai, and Ningxia, etc. (RR 1.65 (95% CI 1.63-1.67)). Conclusion: The spatial clusters of COPD at the city level and regionally varied important risk factors for COPD would help develop tailored interventions for COPD in China. After adjusting for the main risk factors, the spatial clusters of COPD shifted, indicating that there would be other potential risk factors for the remaining clusters which call for furth

关键词： COPD spatial clusters kriging classification and regression tree

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Prediction of financial crises using statistic model and intelligent technologies in ubiquitous environments

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第2期48卷 173-183页

作者： Senoguchi, Junsuke Kurahashi, Setsuya Univ Tsukuba Grad Sch Business Sci Bunkyo Ku 3-29-1 Ootsuka Tokyo 1120012 Japan

A large number of earlier studies partially revealed the mechanism of the financial crises in recent years. However, no study has yet conducted the variable selection from the mounds of factors including the critical component of key macro financial statistics. This study, using a traditional logistic model and intelligent system technologies, explored some key influential factors on the occurrence of the financial crises. As a result, the cyclical component of the current account as percentages of GDP and the cyclical component of the domestic loan as percentages of GDP have been proven a key factor to predict a financial crisis. In the present, China and Malaysia can be classified as a crises group with 77% of possibilities.

关键词： intelligent technologies classification and regression tree CART variable selection artificial intelligence

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Using handwriting to infer a writer's country of origin for forensic intelligence purposes

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FORENSIC SCIENCE INTERNATIONAL 2018年 282卷 144-156页

作者： Agius, Anna Morelato, Marie Moret, Sebastien Chadwick, Scott Jones, Kylie Epple, Rochelle Brown, James Roux, Claude Univ Technol Sydney Ctr Forens Sci POB 123 Broadway 2007 Australia Australian Fed Police Forens Specialist Operat GPO Box 401 Canberra ACT Australia Univ Technol Sydney Sch Math & Phys Sci POB 123 Broadway 2007 Australia

Forensic science has traditionally focused the majority of its resources and objectives towards addressing Court-related questions. However, this view restricts the contribution of forensic science to one process and results in a loss of information as the investigative and intelligence roles are largely neglected. A forensic science discipline suffering from this imbalance is handwriting examination, which may be characterised as a time consuming and subjective process that is mostly carried out towards the end of the investigation for the purpose of judicial proceedings. Individual and habitual characteristics are the major handwriting features exploited, however alternate information concerning the author's native language could potentially be used as a key element in an intelligence framework. This research focussed on the detection of characteristics that differentiate Vietnamese and English Australian writers based on their English handwriting. The study began with the extraction of handwriting characteristics from the writing of people from the two populations. The data was analysed using a logistic regression model and a classification and regression tree (CRT). Each recognised four class characteristics that were capable of distinguishing between the two nationalities. The logistic regression and CRT models were both capable of correctly predicting 93% of cases. Their predictive capabilities were then tested and supported using blind exemplars in order to mirror casework settings. It appeared that when using their respective class characteristics, the two models were capable of differentiating English Australians from Vietnamese in the data set. This proof of concept research demonstrated the plausibility of exploiting this additional information from a handwriting trace and taking advantage of it in an intelligence-led framework. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Documents Writers Intelligence Logistic regression model classification and regression tree

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Is it possible to identify cases of coronary artery bypass graft postoperative surgical site infection accurately from claims data?

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2014年第1期14卷 1-9页

作者： Yu, Tsung-Hsien Hou, Yu-Chang Lin, Kuan-Chia Chung, Kuo-Piao Natl Taiwan Univ Inst Healthcare Policy & Management Taipei 10764 Taiwan Natl Taiwan Univ Master Degree Program Publ Hlth Taipei 10764 Taiwan Minist Hlth & Welf Tao Yuan Gen Hosp Dept Chinese Med Taoyuan Taiwan Chuan Yuan Christian Univ Dept Biosci Technol Taoyuan Taiwan Natl Taipei Univ Nursing & Hlth Sci Dept Hlth Care & Management Taipei Taiwan

Background: Claims data has usually been used in recent studies to identify cases of healthcare-associated infection. However, several studies have indicated that the ICD-9-CM codes might be inappropriate for identifying such cases from claims data;therefore, several researchers developed alternative identification models to correctly identify more cases from claims data. The purpose of this study was to investigate three common approaches to develop alternative models for the identification of cases of coronary artery bypass graft (CABG) surgical site infection, and to compare the performance between these models and the ICD-9-CM model. Methods: The 2005-2008 National Health Insurance claims data and healthcare-associated infection surveillance data from two medical centers were used in this study for model development and model verification. In addition to the use of ICD-9-CM codes, this study also used classification algorithms, a multivariable regression model, and a decision tree model in the development of alternative identification models. In the classification algorithms, we defined three levels (strict, moderate, and loose) of the criteria in terms of their strictness. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were used to evaluate the performance of each model. Results: The ICD-9-CM-based model showed good specificity and negative predictive value, but sensitivity and positive predictive value were poor. Performances of the other models were varied, except for negative predictive value. Among the models, the performance of the decision tree model was excellent, especially in terms of positive predictive value. Conclusion: The accuracy of identification of cases of CABG surgical site infection is an important issue in claims data. Use of the decision tree model to identify such cases can improve the accuracy of patient-level outcome research. This model should be considered when performing future research u

关键词： Administrative data Identification model CABG Surgical site infection Decision tree classification and regression tree

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