Browsing by Author "Liang, Zhiying"

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    Correction to: Fluid handling and blood flow patterns in neonatal respiratory distress syndrome versus transient tachypnea: a pilot study
    (2022-01-19) Ismail, Rana; Murthy, Prashanth; Abou Mehrem, Ayman; Liang, Zhiying; Stritzke, Amelie
    An amendment to this paper has been published and can be accessed via the original article.
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    Personalized 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.
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    SELF-STABILIZING MINIMUM SPANNING TREE CONSTRUCTION ON MESSAGE-PASSING NETWORK
    (2001-11-14) Liang, Zhiying
    Self-stabilization is an abstraction of fault tolerance for transient faults. It guarantees that the system will eventually reach a legitimate configuration when started from an arbitrary initial configuration. This thesis presents two minimum spanning tree algorithms designed directly for deterministic, message-passing networks. The first converts an arbitrary spanning tree to a minimum one; the second is a fully self-stabilizing construction. The algorithms assume distinct identifiers and reliable fifo message passing, but do not rely on a root or synchrony. Also, processors have a safe time-out mechanism (the minimum assumption necessary for a solution to exist). Both algorithms apply to networks that can change dynamically.
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