Browsing by Author "Feng, Yuanchao"
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- ItemOpen AccessIntelligent Nanocoatings for Corrosion Protection of Steels(2017) Feng, Yuanchao; Cheng, Frank; Omanovic, Sasha; Song, Hua; Egberts, Philip; Wang, Shenghui; Cheng, FrankIntelligent coatings, also called smart coatings, refer to coating systems capable of sensing the generation of corrosive environments, and self-responding to corrosion occurrence in demand. In this research, an intelligent coating technology based on doping of home-prepared nanocontainers pre-loaded with inhibitors- in an epoxy host coating was developed for effective corrosion protection of pipeline steel. The performance of benzotriazole (BTA) inhibitors on preventing corrosion of an X65 pipeline steel was investigated in a bicarbonate solution. A layer of protective inhibitor film with a roughness of nano-meter scale was formed on the steel surface, inhibiting corrosion of the steel. To determine the compatibility of nanocontainers with the host coating, multi-layered Halloysite polyelectrolyte nanocontainers were fabricated and doped in an epoxy coating. The coating containing Halloysite nanocontainers possessed enhanced corrosion resistance. The corrosion resistance of the coating was improved with the increasing content of the Halloysite nanocontainers in the coating. To improve the ability of nanocontainers to encapsulate inhibitors BTA, a SiO2 nanoparticle based polyelectrolyte assembly was prepared as the BTA-encapsulating nanocontainers. At either low or high pH value (e.g., pH 2 or 11), the BTA was released to prevent steel from corrosion in chloride solutions. The Korsemeyer-Peppas model provided an estimation of the inhibitor-releasing rate, which served as the base for prediction of the service life of the intelligent coatings. An intelligent coating was developed by doping the BTA-encapsulated, SiO2 nano-particle-based polyelectrolyte nanocontainers in the epoxy coating. For the pipeline steel coated with the intelligent coatings, the corrosion inhibition was time dependent upon self-releasing of the encapsulated inhibitors from the nanocontainers. With the increasing content of the BTA-encapsulated nanocontainers in the coating, both the coating resistance and corrosion resistance of the steel increase, resulting in a reduced corrosion of the steel. Furthermore, superhydrophobic zinc nano-films were fabricated on X65 pipeline steel. The optimal condition for electrodeposition was under the current density of 100 mA/cm2 for 20 mins. The fabricated superhydrophobic, which had a water contact angle up to 158.4° ± 1.5°, possessed a satisfactory antifouling and self-cleaning ability, and provided an effective corrosion protection to the steel in a chloride solution.
- ItemOpen AccessNew method for determining breast cancer recurrence-free survival using routinely collected real-world health data(2022-03-16) Jung, Hyunmin; Lu, Mingshan; Quan, May L.; Cheung, Winson Y.; Kong, Shiying; Lupichuk, Sasha; Feng, Yuanchao; Xu, YuanAbstract Background In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence. Methods Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review. Results In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan–Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar. Conclusion The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis.
- ItemOpen AccessPersonalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning(2022-12-17) Feng, Yuanchao; Leung, Alexander A.; Lu, Xuewen; Liang, Zhiying; Quan, Hude; Walker, Robin L.Abstract Background Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients. Methods Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score. Results The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction. Conclusions This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.
- ItemOpen AccessPersonalized survival prediction of cardiovascular disease among hypertensive patients: a machine learning approach based on health administrative data(2020-12-22) Feng, Yuanchao; Quan, Hude; Walker, Robin L.; Leung, Alexander; Lu, XuewenBackground: Cardiovascular disease (CVD) kills approximately 17 million people globally every year, and they mainly exhibit myocardial infarctions, heart failure and stroke. Hypertension is the leading risk factor for premature death from CVD. Available routinely collected administrative health data with demographic features, comorbidities information, clinical laboratory test values and medication usage results can be used to perform biostatistics analysis aimed at highlights and correlations otherwise undetectable by medical doctors. Machine learning algorithms can predict patients’ survival by using information recorded in their medical records. Objective: To compare the performance of four machine learning approaches on personalized survival prediction of CVD outcomes among newly diagnosed hypertensive patients. Method: Hypertension cohort, CVD outcomes, and covariates were defined using validated case definitions applied to inpatient and outpatient administrative health databases. We analyzed a cohort of 11863 CVD events among 259,873 newly diagnosed hypertensive patients from April 1, 2009 to March 31, 2015 and had at least one-year follow-up. We applied linear multi-task logistic regression (LMTLR), neural multi-task logistic regression (NMTLR), random survival forest (RSF) and Cox proportional hazard (CoxPH) models to both predict the number of CVD outcomes in each survival time point and predict individual survival probability curve. The predictive performance was evaluated by root mean squared error (RMSE), mean absolute error (MAE), concordance index (C-index) and Brier score. Results: Our results show that the RSF model has the lowest RMSE value at 33.94 and lowest MAE value at 28.37, which means it has the better performance to predict the number of CVD events at any time point during the follow-up period. NMTLR model has the highest C-index at 0.8149 and lowest Brier score at 0.0242 for the individual survival prediction. Conclusions: This is the first personalized survival prediction for CVD among hypertensive patients using administrative data. The four models tested in this analysis (LMTLR, NMTLR, RSF, CoxPH) exhibited similar discrimination and calibration ability in predicting the survival of hypertension patients. In the test dataset, RSF has better performance for population-based survival prediction while the NMTLR had better discrimination and calibration for individual-based survival prediction.