Accurate prediction and further dissection of neonicotinoid elimination in the water treatment by CTS@AgBC using multihead attention-based convolutional neural network combined with the time-dependent Cox regression model

The Cox regression model, which is widely used for the analysis of factor effects with censored survival data, makes the assumption of constant hazard ratio. Different methods should be used to deal with non-proportionality of hazards when this assumption is violated. In this study, we use the Extended Cox regression model where time dependent covariate terms with fixed functions of time are considered. Time to first birth for the ever married women after marriage, taken from BDHS 2011 women data is fitted using Extended Cox regression model due to the failure of existence of proportionality assumption. This model performs well as expected compared to Cox regression model. DOI: http://dx.doi.org/10.3329/dujs.v63i1.21764 Dhaka Univ. J. Sci. 63(1): 25-30, 2015 (January)

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813. The Reduction of the acquisition rate of carbapenem resistant Acinetobacter baumannii after room privatization in the intensive care unit

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1002 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S448-S449

Author(s):

Jongtak Jung ◽

Pyoeng Gyun Choe ◽

Chang Kyung Kang ◽

Kyung Ho Song ◽

Wan Beom Park ◽

...

Keyword(s):

Regression Model ◽

Acinetobacter Baumannii ◽

Cox Regression ◽

Univariate Analysis ◽

Intervention Group ◽

Feeding Tube ◽

Control Group ◽

Cox Regression Model ◽

Carbapenem Resistant ◽

Hospital Acquired

Abstract Background Acinetobacter baumannii is one of the major pathogens of hospital-acquired infection recently and hospital outbreaks have been reported worldwide. On September 2017, New intensive care unit(ICU) with only single rooms, remodeling from old ICU with multibed bay rooms, was opened in an acute-care tertiary hospital in Seoul, Korea. We investigated the effect of room privatization in the ICU on the acquisition of carbapenem-resistant Acinetobacter baumannii(CRAB). Methods We retrospectively reviewed medical records of patients who admitted to the medical ICU in a tertiary care university-affiliated 1,800-bed hospital from 1 January 2015 to 1 January 2019. Patients admitted to the medical ICU before the remodeling of the ICU were designated as the control group, and those who admitted to the medical ICU after the remodeling were designated as the intervention group. Then we compared the acquisition rate of CRAB between the control and intervention groups. Patients colonized with CRAB or patients with CRAB identified in screening tests were excluded from the study population. The multivariable Cox regression model was performed using variables with p-values of less than 0.1 in the univariate analysis. Results A total of 1,105 cases admitted to the ICU during the study period were analyzed. CRAB was isolated from 110 cases in the control group(n=687), and 16 cases in the intervention group(n=418). In univariate analysis, room privatization, prior exposure to antibiotics (carbapenem, vancomycin, fluoroquinolone), mechanical ventilation, central venous catheter, tracheostomy, the presence of feeding tube(Levin tube or percutaneous gastrostomy) and the length of ICU stay were significant risk factors for the acquisition of CRAB (p< 0.05). In the multivariable Cox regression model, the presence of feeding tube(Hazard ratio(HR) 4.815, 95% Confidence interval(CI) 1.94-11.96, p=0.001) and room privatization(HR 0.024, 95% CI 0.127-0.396, p=0.000) were independent risk factors. Table 1. Univariate analysis of Carbapenem-resistant Acinetobacter baumannii Table 2. Multivariable Cox regression model of the acquisition of Carbapenem-resistant Acinetobacter baumannii Conclusion In the present study, room privatization of the ICU was correlated with the reduction of CRAB acquisition independently. Remodeling of the ICU to the single room would be an efficient strategy for preventing the spreading of multidrug-resistant organisms and hospital-acquired infection. Disclosures All Authors: No reported disclosures

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Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

Applied Sciences ◽

10.3390/app11146594 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6594

Author(s):

Yu-Chia Hsu

Keyword(s):

Neural Network ◽

Time Series ◽

Pattern Recognition ◽

Logistic Regression ◽

Regression Model ◽

Convolutional Neural Network ◽

Logistic Regression Model ◽

National Football League ◽

Performance Metrics ◽

Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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