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Predicting depressed patients with suicidal ideation from ECG recordings

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2017年第5期55卷 793-805页

作者： Khandoker, A. H. Luthra, V. Abouallaban, Y. Saha, S. Ahmed, K. I. Mostafa, R. Chowdhury, N. Jelinek, H. F. Khalifa Univ Dept Biomed Engn POB 127788 Abu Dhabi U Arab Emirates Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Amer Ctr Psychiat & Neurol Abu Dhabi U Arab Emirates United Int Univ Dhaka Bangladesh Charles Sturt Univ Sch Community Hlth Albury NSW Australia Charles Sturt Univ Ctr Res Complex Syst Albury NSW Australia Macquarie Univ Australian Sch Adv Med Sydney NSW Australia

Globally suicidal behavior is the third most common cause of death among patients with major depressive disorder (MDD). This study presents multi-lag tone-entropy (T-E) analysis of heart rate variability (HRV) as a screening tool for identifying MDD patients with suicidal ideation. Sixty-one ECG recordings (10 min) were acquired and analyzed from control subjects (29 CONT), 16 MDD subjects with (MDDSI+) and 16 without suicidal ideation (MDDSI-). After ECG preprocessing, tone and entropy values were calculated for multiple lags (m: 1-10). The MDDSI+ group was found to have a higher mean tone value compared to that of the MDDSI- group for lags 1-8, whereas the mean entropy value was lower in MDDSI+ than that in CONT group at all lags (1-10). Leave-one-out cross-validation tests, using a classification and regression tree (CART), obtained 94.83 % accuracy in predicting MDDSI+ subjects by using a combination of tone and entropy values at all lags and including demographic factors (age, BMI and waist circumference) compared to results with time and frequency domain HRV analysis. The results of this pilot study demonstrate the usefulness of multi-lag T-E analysis in identifying MDD patients with suicidal ideation and highlight the change in autonomic nervous system modulation of the heart rate associated with depression and suicidal ideation.

关键词： Major depressive disorder Heart rate variability Tone-entropy analysis classification and regression tree Suicidal ideation

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Comparison of Spatial Interpolation Methods for Gridded Bias Removal in Surface Temperature Forecasts

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Journal of Meteorological Research 2017年第4期31卷 791-799页

作者： Seyedeh Atefeh MOHAMMADI Majid AZADI Morteza RAHMANI Technology Development Institute Tehran 13445-686 Iran Atmospheric Science and Meteorological Research Center Tehran 16385-14977 Iran Faculty of Basic and Advanced Technologies in Biology University of Science and Culture Tehran 14619-68151 Iran

All numerical weather prediction（NWP） models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate（IDSW-CLR）, Kriging with constant lapse rate（Kriging-CLR）, gradient inverse distance squared with linear lapse rate（GIDS-LR）, and gradient inverse distance squared with lapse rate determined by classification and regression tree（GIDS-CART）, are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly,but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error（RMSE）and mean absolute error（MAE） were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.

关键词： spatial interpolation bias correction lapse rate Kriging classification and regression tree

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Liquid floodback detection for scroll compressor in a VRF system under heating mode

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APPLIED THERMAL ENGINEERING 2017年 114卷 921-930页

作者： Wang, Jiangyu Li, Guannan Chen, Huanxin Liu, Jiangyan Guo, Yabin Hu, Yunpeng Li, Jiong Huazhong Univ Sci & Technol Sch Energy & Power Engn Dept Refrigerat & Cryogen Engn Wuhan 430074 PR Peoples R China Wuhan Business Univ Wuhan 430056 PR Peoples R China State Key Lab Compressor Technol Hefei 230031 PR Peoples R China

Liquid floodback leads to negative consequences, such as extra energy consumption, comfort degradation for building occupants, and mechanical failure of compressors. Fault detection and diagnosis of liquid floodback at incipient stage are necessary for variable refrigerant flow (VRF) systems. This study proposes decision tree models to detect and diagnose the liquid floodback of the scroll compressor in a VRF system. First, feature selection through correlation analysis was performed to remove redundant variables. Second, classification and regression tree (CART) was used to develop decision tree models for detecting liquid floodback. Third, two faults that cause liquid floodback were examined, and another CART model was established to diagnose fault causes when a fault occurred. The CART models demonstrated desirable performance in floodback FDD. The rules extrapolated from the two data-driven models were in agreement with domain knowledge. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： classification and regression tree Fault detection and diagnosis Variable refrigerant flow Refrigerant overcharge Compressor liquid floodback Heating mode

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Data mining: Potential applications in research on nutrition and health

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NUTRITION & DIETETICS 2017年第1期74卷 3-10页

作者： Batterham, Marijka Neale, Elizabeth Martin, Allison Tapsell, Linda Univ Wollongong Natl Inst Appl Stat Res Australia Stat Consulting Ctr Wollongong NSW Australia Univ Wollongong Fac Sci Med & Hlth Sch Med Wollongong NSW Australia

Aim: Data mining enables further insights from nutrition-related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition-related intervention. Methods: Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. Results: The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables' importance. CART found that body fat, quality of life using the SF-12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF-12PCS, glucose levels and level of education to be the most significant predictors (P = 0.01). Conclusions: Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research.

关键词： artificial neural network classification and regression tree data mining support vector machine

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Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China

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BMC INFECTIOUS DISEASES 2018年第1期18卷 15-15页

作者： Gou, Faxiang Liu, Xinfeng He, Jian Liu, Dongpeng Cheng, Yao Liu, Haixia Yang, Xiaoting Wei, Kongfu Zheng, Yunhe Jiang, Xiaojuan Meng, Lei Hu, Wenbiao Gansu Ctr Dis Prevent & Control Inst Communicable Dis Control & Prevent Lanzhou Gansu Peoples R China Queensland Univ Technol Inst Hlth & Biomed Innovat Sch Publ Hlth & Social Work Brisbane Qld Australia

Background: To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission. Method: Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways. Results: GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2. 8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 degrees C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 degrees C of Jiuquan to 16 degrees C in Tianshui;and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui. Conclusion: Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan;Relative humidity's effect on HFMD showed a non-linear interacting relationship with average temperature.

关键词： Hand foot and mouth disease Climate change Generalized linear model classification and regression tree

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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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An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data

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GEOCARTO INTERNATIONAL 2017年第2期32卷 128-138页

作者： Sonobe, Rei Tani, Hiroshi Wang, Xiufeng Shizuoka Univ Fac Agr Shizuoka Japan Hokkaido Univ Res Fac Agr Sapporo Hokkaido Japan

The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7;a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries-Matusita (J-M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data).

关键词： classification and regression tree kernel-based extreme learning machine Landsat-8 operational land imager

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Characterizing user engagement with health app data: a data mining approach

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TRANSLATIONAL BEHAVIORAL MEDICINE 2017年第2期7卷 277-285页

作者： Serrano, Katrina J. Coa, Kisha I. Yu, Mandi Wolff-Hughes, Dana L. Atienza, Audie A. NCI NIH Rockville MD 20850 USA ICF Int Rockville MD USA NIH Off Behav & Social Sci Res Bldg 10 Bethesda MD 20892 USA

The use of mobile health applications (apps) especially in the area of lifestyle behaviors has increased, thus providing unprecedented opportunities to develop health programs that can engage people in real-time and in the real-world. Yet, relatively little is known about which factors relate to the engagement of commercially available apps for health behaviors. This exploratory study examined behavioral engagement with a weight loss app, Lose It! and characterized higher versus lower engaged groups. Cross-sectional, anonymized data from Lose It! were analyzed (n = 12,427,196). This dataset was randomly split into 24 subsamples and three were used for this study (total n = 1,011,008). classification and regression tree methods were used to identify subgroups of user engagement with one subsample, and descriptive analyses were conducted to examine other group characteristics associated with engagement. Data mining validation methods were conducted with two separate subsamples. On average, users engaged with the app for 29 days. Six unique subgroups were identified, and engagement for each subgroup varied, ranging from 3.5 to 172 days. Highly engaged subgroups were primarily distinguished by the customization of diet and exercise. Those less engaged were distinguished by weigh-ins and the customization of diet. Results were replicated in further analyses. Commercially-developed apps can reach large segments of the population, and data from these apps can provide insights into important app features that may aid in user engagement. Getting users to engage with a mobile health app is critical to the success of apps and interventions that are focused on health behavior change.

关键词： Mobile health application Mobile health technology Smartphone app Data mining Big data User engagement classification and regression tree

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Plant community change in three Mongolian steppe ecosystems 1994-2013: applications to state-and-transition models

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ECOSPHERE 2018年第3期9卷

作者： Jamiyansharav, Khishigbayar Fernandez-Gimenez, Maria E. Angerer, Jay P. Yadamsuren, Baasandorj Dash, Zumberelmaa Colorado State Univ Warner Coll Nat Resources Ft Collins CO 80523 USA Texas A&M Agrilife Res Blackland Res & Extens Ctr Temple TX 76502 USA Mongolian Acad Sci Inst Geog & Geoecol POB 81 Ulaanbaatar 211238 Mongolia Mongolian Acad Sci Inst Bot Amariin Gudamj 1 Ulaanbaatar 210620 Mongolia

Interacting effects of climate change and livestock grazing on semi-arid grassland ecosystems have not been well studied, especially on a long-term basis. This paper analyzes changes in plant community composition in relation to grazing intensity and climate change based on repeated monitoring along long-term grazing intensity gradients in three Mongolian ecological zones over 20 yr. We synthesized our findings into state-and-transition models of vegetation change, contributing to our understanding of ecological dynamics in relation to management and environmental change, and to the development of tools for resilience-based rangeland management. In the mountain steppe (MS), community composition was driven largely by climate, and transitions from one community to another were associated with climate change or combined climate and grazing effects. The MS experienced the largest number of long-term transitions (14 of 15 plots) over 20 yr. In the steppe (ST), grazing intensity was the strongest influence on community composition, but transitions between communities from the early 1990s to 2013 were most strongly correlated with climate change. Ten of the 15 ST plots transitioned to other communities over 20 yr. Community composition in the desert steppe (DS) was unrelated to either grazing intensity or climate change and only six of 15 plots transitioned permanently over 20 yr. The MS appears most vulnerable to climate-induced community change, as others have suggested. Some degraded ST communities are resilient to climate change, while ST communities on drier sites are vulnerable to grazing-induced community changes. Our findings illustrate the utility of state-and-transition models as a means to synthesize and depict plant community dynamics in relation to climate and management factors. These models identify communities that may be growing rarer or more common under the combined effects of climate change and grazing, and highlight species and communities that may be

关键词： classification classification and regression tree climate change livestock grazing Mongolia ordination rangeland resilience-based management semi-arid grassland

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