Biomedical subjects

Tze-Yun Leong

Publications and source records attributed to Tze-Yun Leong.

4 recordsLinked to original sources

PubMed2005

PGMC: a framework for probabilistic graphic model combination.

Decision making in biomedicine often involves incorporating new evidences into existing or working models reflecting the decision problems at hand. We propose a new framework that facilitates effective and incremental integration of multiple probabilistic graphical models. The proposed framework aims to minimize time and effort required to customize and extend the original models through preserving the conditional independence relationships inherent in two types of probabilistic graphical models: Bayesian networks and influence diagrams. We present a four-step algorithm to systematically combine the qualitative and the quantitative parts of the different models; we also describe three heuristic methods for target variable generation to reduce the complexity of the integrated models. Preliminary results from a case study in heart disease diagnosis demonstrate the feasibility and potential for applying the proposed framework in real applications.

Algorithms↗

PubMed2004

Cost-effectiveness analysis of colorectal cancer screening strategies in Singapore: a dynamic decision analytic approach.

A dynamic decision analytic framework using local statistics and expert's opinions is put to study the cost-effectiveness of colorectal cancer screening strategies in Singapore. It is demonstrated that any of the screening strategies, if implemented, would increase the life expectancy of the population of 50 to 70 years old. The model also determined the normal life expectancy of this population to be 76.32 years. Overall, Guaiac Fecal Occult Blood Test (FOBT) is most cost effective at SGD162.11 per life year saved per person. Our approach allowed us to model problem parameters that change over time and study the utility measures like cost and life expectancy for specific age within the range of 50- 69 through to 70 years old.

Aged↗

PubMed2004

We did the right thing: an intervention analysis approach to modeling intervened SARS propagation in Singapore.

In this paper, we adopt the Intervention Analysis approach to model an intervened natural process, i.e., propagation of the severe acute respiratory syndrome (SARS) in Singapore, which is affected not only by its own evolutionary history but also by the control measures taken. Using this model, the propagation trend of the epidemic and the effects of different control measures on the outcomes of this epidemic can be simulated and quantitatively analyzed. Based on the model, we have performed an evaluation and sensitivity analysis of the Singapore government's responses to this epidemic. Preliminary results have shown that the control measures taken are effective in controlling the outbreak.

Communicable Disease Control↗

PubMed2003

Characterization of medical time series using fuzzy similarity-based fractal dimensions.

This paper attempts to characterize medical time series using fractal dimensions. Existing fractal dimensions like box, information and correlation dimensions characterize the time series by measuring the rate at which the distribution of the time series changes when the length (or radius) of the box (or hypersphere) is changed. However, the measured dimensions significantly vary when the box (or hypersphere) position is changed slightly. It happens because the data points just outside the box (or hypersphere) are not accounted for, and all the data points inside the box or hypersphere are treated equally. To overcome these problems, the hypersphere is converted to a Gaussian, and thus the hard boundary becomes soft. The Gaussian represents the fuzzy similarity between the neighbors and the point around which the Gaussian is constructed. This concept of similarity is exploited to propose a fuzzy similarity-based fractal dimension. The proposed dimension aims to capture the regularity of the time series in terms of how the fuzzy similarity scales up/down when the resolution of the time series is decreased/increased. Experiments on intensive care unit (ICU) data sets show that the proposed dimension characterizes the time series better than the correlation dimension.

Algorithms↗