Publications by authors named "Tze Yun Leong"

29 Publications

  • Page 1 of 1

Subdomain Adaptation With Manifolds Discrepancy Alignment.

IEEE Trans Cybern 2021 May 13;PP. Epub 2021 May 13.

Reducing domain divergence is a key step in transfer learning. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this article, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use the low-dimensional manifold to represent the subdomain, and align the local data distribution discrepancy in each manifold across domains. A manifold maximum mean discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called transfer with manifolds discrepancy alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Experimental studies show that TMDA is a promising method for various transfer learning tasks.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCYB.2021.3071244DOI Listing
May 2021

Digital Health during COVID-19: Informatics Dialogue with the World Health Organization.

Yearb Med Inform 2021 Apr 21. Epub 2021 Apr 21.

eHealth Development Association, Amman, Jordan.

Background: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic.

Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs).

Methods: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants' consent and summarized in this paper.

Results: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general; (b) ethics and artificial intelligence; (c) digital divide; (d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic.

Conclusions: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1055/s-0041-1726480DOI Listing
April 2021

Modeling Multi-View Dependence in Bayesian Networks for Alzheimer's Disease Detection.

Stud Health Technol Inform 2019 Aug;264:358-362

School of Computing, National University of Singapore, Singapore.

Early detection of Alzheimer's disease is important for deploying interventions to prevent or slow disease progression. We propose a multi-view dependence modeling framework that integrates multiple data sources to distinguish patients at different stages of the disease. We design interpretable models that can handle heterogeneous data types including neuro-images, bio- and clinical markers, and historical and genotypical characteristics of the subjects. We learn the dependence structure from data with guidance from domain knowledge in Bayesian Networks, visualizing and quantifying the conditional probabilistic dependence among the variables. Our results indicate that the hybrid dependence models also improve prediction performance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3233/SHTI190243DOI Listing
August 2019

Knowledge-Driven Interpretation of Multi-View Data in Medicine.

Stud Health Technol Inform 2018 ;247:745-749

School of Computing, National University of Singapore, Singapore.

We propose a new approach to clinical decision support with interpretable recommendations from multi-view data. We introduce a Bayesian network structure learning method to help identify the relevant factors and their relationships. Guided by minimal domain knowledge, this method highlights the significant associations among the demography, medical and family history, lifestyle, and biomarker data to facilitate informed clinical decisions. We demonstrate the effectiveness of the method for detecting mild neurocognitive disorder in the elderly from a real-life dataset in Singapore. The empirical results show that our method achieves better interpretability in addition to comparable accuracy with respect to the benchmark studies.
View Article and Find Full Text PDF

Download full-text PDF

Source
June 2018

A Deep Learning Approach to Neuroanatomical Characterisation of Alzheimer's Disease.

Stud Health Technol Inform 2017 ;245:1249

Medical Computing Laboratory, School of Computing, National University of Singapore, Singapore.

Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain. The proposed technique has a classification accuracy of 81.79% for AD against healthy subjects using a single modality imaging dataset.
View Article and Find Full Text PDF

Download full-text PDF

Source
June 2018

Research Strategies for Biomedical and Health Informatics. Some Thought-provoking and Critical Proposals to Encourage Scientific Debate on the Nature of Good Research in Medical Informatics.

Methods Inf Med 2017 Jan 25;56(S 01):e1-e10. Epub 2017 Jan 25.

Background: Medical informatics, or biomedical and health informatics (BMHI), has become an established scientific discipline. In all such disciplines there is a certain inertia to persist in focusing on well-established research areas and to hold on to well-known research methodologies rather than adopting new ones, which may be more appropriate.

Objectives: To search for answers to the following questions: What are research fields in informatics, which are not being currently adequately addressed, and which methodological approaches might be insufficiently used? Do we know about reasons? What could be consequences of change for research and for education?

Methods: Outstanding informatics scientists were invited to three panel sessions on this topic in leading international conferences (MIE 2015, Medinfo 2015, HEC 2016) in order to get their answers to these questions.

Results: A variety of themes emerged in the set of answers provided by the panellists. Some panellists took the theoretical foundations of the field for granted, while several questioned whether the field was actually grounded in a strong theoretical foundation. Panellists proposed a range of suggestions for new or improved approaches, methodologies, and techniques to enhance the BMHI research agenda.

Conclusions: The field of BMHI is on the one hand maturing as an academic community and intellectual endeavour. On the other hand vendor-supplied solutions may be too readily and uncritically accepted in health care practice. There is a high chance that BMHI will continue to flourish as an important discipline; its innovative interventions might then reach the original objectives of advancing science and improving health care outcomes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3414/ME16-01-0125DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388922PMC
January 2017

Fusing Heterogeneous Data for Alzheimer's Disease Classification.

Stud Health Technol Inform 2015 ;216:731-5

Medical Computing Laboratory, School of Computing, National University of Singapore, Singapore.

In multi-view learning, multimodal representations of a real world object or situation are integrated to learn its overall picture. Feature sets from distinct data sources carry different, yet complementary, information which, if analysed together, usually yield better insights and more accurate results. Neuro-degenerative disorders such as dementia are characterized by changes in multiple biomarkers. This work combines the features from neuroimaging and cerebrospinal fluid studies to distinguish Alzheimer's disease patients from healthy subjects. We apply statistical data fusion techniques on 101 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We examine whether fusion of biomarkers helps to improve diagnostic accuracy and how the methods compare against each other for this problem. Our results indicate that multimodal data fusion improves classification accuracy.
View Article and Find Full Text PDF

Download full-text PDF

Source
December 2016

Automatic detection and quantification of brain midline shift using anatomical marker model.

Comput Med Imaging Graph 2014 Jan 26;38(1):1-14. Epub 2013 Nov 26.

National Neuroscience Institute, Tan Tock Seng Hospital, Singapore.

Brain midline shift (MLS) is a significant factor in brain CT diagnosis. In this paper, we present a new method of automatically detecting and quantifying brain midline shift in traumatic injury brain CT images. The proposed method automatically picks out the CT slice on which midline shift can be observed most clearly and uses automatically detected anatomical markers to delineate the deformed midline and quantify the shift. For each anatomical marker, the detector generates five candidate points. Then the best candidate for each marker is selected based on the statistical distribution of features characterizing the spatial relationships among the markers. Experiments show that the proposed method outperforms previous methods, especially in the cases of large intra-cerebral hemorrhage and missing ventricles. A brain CT retrieval system is also developed based on the brain midline shift quantification results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.compmedimag.2013.11.001DOI Listing
January 2014

From the Scientific Program Chairs. Preface.

Stud Health Technol Inform 2013 ;192

View Article and Find Full Text PDF

Download full-text PDF

Source
April 2015

Unsupervised medical image classification by combining case-based classifiers.

Stud Health Technol Inform 2013 ;192:739-43

School of Computing, National University of Singapore, Singapore.

We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classifiers in an ensemble classification framework. The unsupervised nature of this non-parametric technique can significantly reduce the time and effort for system calibration. In particular, classification of medical images in this framework does not rely on segmentation, nor semantic-based or annotation-based feature selection. Our experiments show very promising results in classifying computer tomography image slices into pathological classes for traumatic brain injury patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
April 2015

An automated pathological class level annotation system for volumetric brain images.

AMIA Annu Symp Proc 2012 3;2012:1201-10. Epub 2012 Nov 3.

National University of Singapore, Singapore.

We introduce an automated, pathological class level annotation system for medical volumetric brain images. While much of the earlier work has mainly focused on annotating regions of interest in medical images, our system does not require annotated region level training data nor assumes perfect segmentation results for the regions of interest; the time and effort needed for acquiring training data are hence significantly reduced. This capability of handling high-dimensional noisy data, however, poses additional technical challenges, since statistical estimation of models for such data is prone to over-fitting. We propose a framework that combines a regularized logistic regression method and a kernel-based discriminative method to address these problems. Regularized methods provide a flexible selection mechanism that is well-suited for high dimensional noisy data. Our experiments show promising results in classifying computer tomography images of traumatic brain injury patients into pathological classes.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540549PMC
July 2013

A generative model based approach to retrieving ischemic stroke images.

AMIA Annu Symp Proc 2011 22;2011:312-21. Epub 2011 Oct 22.

National University of Singapore, Singapore.

This paper proposes a generative model approach to automatically annotate medical images to improve the efficiency and effectiveness of image retrieval systems for teaching, research, and diagnosis. The generative model captures the probabilistic relationships among relevant classification tags, tentative lesion patterns, and selected input features. Operating on the imperfect segmentation results of input images, the probabilistic framework can effectively handle the inherent uncertainties in the images and insufficient information in the training data. Preliminary assessment in the ischemic stroke subtype classification shows that the proposed system is capable of generating the relevant tags for ischemic stroke brain images. The main benefit of this approach is its scalability; the method can be applied in large image databases as it requires only minimal manual labeling of the training data and does not demand high-precision segmentation of the images.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243197PMC
February 2013

Efficient mining of haplotype patterns for linkage disequilibrium mapping.

J Bioinform Comput Biol 2010 Dec;8 Suppl 1:127-46

School of Computing, National University of Singapore, Singapore.

Effective identification of disease-causing gene locations can have significant impact on patient management decisions that will ultimately increase survival rates and improve the overall quality of health care. Linkage disequilibrium mapping is the process of finding disease gene locations through comparisons of haplotype frequencies between disease chromosomes and normal chromosomes. This work presents a new method for linkage disequilibrium mapping. The main advantage of the proposed algorithm, called LinkageTracker, is its consistency in producing good predictive accuracy under different conditions, including extreme conditions where the occurrence of disease samples with the mutation of interest is very low and there is presence of error or noise. We compared our method with some leading methods in linkage disequilibrium mapping such as HapMiner, Blade, GeneRecon, and Haplotype Pattern Mining (HPM). Experimental results show that for a substantial class of problems, our method has good predictive accuracy while taking reasonably short processing time. Furthermore, LinkageTracker does not require any population ancestry information about the disease and the genealogy of the haplotypes. Therefore, it is useful for linkage disequilibrium mapping when the users do not have such information about their datasets.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1142/s0219720010005142DOI Listing
December 2010

A model driven approach to imbalanced data sampling in medical decision making.

Stud Health Technol Inform 2010 ;160(Pt 2):856-60

Medical Computing Lab, School of Computing, National University of Singapore, Singapore.

Classification is an important medical decision support function that can be seriously affected by disproportionate class distribution in the training data. In medical decision making, the rate of misclassification and the cost of misclassifying a minority (positive) class as a majority (negative) class are especially high. In this paper, we propose a new model-driven sampling approach to balancing data samples. Most existing data sampling methods produce new data points based on local, deterministic information. Our approach extends the idea of generative sampling to produce new data points based on an induced probabilistic graphical model. We present the motivation and the design of the proposed algorithm, and compare it with two representative imbalanced data sampling approaches on four medical data sets varying in size, imbalance ratio, and dimension. The empirical study helped identify the challenges in imbalanced data problems in medicine, and highlighted the strengths and limitations of the relevant sampling approaches. Performance of the model driven approach is shown to be comparable with existing approaches; potential improvements could be achieved by incorporating domain knowledge.
View Article and Find Full Text PDF

Download full-text PDF

Source
April 2011

Towards a multi-level game model for influenza epidemics.

Stud Health Technol Inform 2010 ;160(Pt 1):457-61

Medical Computing Laboratory, School of Computing, National University of Singapore, Republic of Singapore.

Although game theory has been first invented to reason with economic scenarios with rational agents, it has since been extended into many other fields including biological and medical sciences. In this paper we propose to model the interactions between virus and human in an influenza epidemic in a two player, adversarial game scenario with multiple levels of abstraction. As conventional game representations are inadequate in this complex problem domain, we propose Object Oriented Multi-Agent Influence Diagrams (OO-MAID), a novel graphical representation for multi-level games, which takes advantage of both organizational information and probabilistic independence in the problem domain. The OO-MAID representation can be readily applied in similar medical independent characteristics. We demonstrate the feasibility of this novel approach with sample models in the domain.
View Article and Find Full Text PDF

Download full-text PDF

Source
December 2010

Writing for publication in biomedical informatics.

AMIA Annu Symp Proc 2008 Nov 6:1229-32. Epub 2008 Nov 6.

Dept. of Biomedical Informatics & Emergency Medicine, Vanderbilt University, 2209 Garland Ave, Nashville, TN 37232-8340, USA.

Writing for publication can be a rewarding activity for researchers at all levels of experience. However, many students and researchers are less familiar with the various aspects of the publication process. The purpose of this workshop is to provide participants with the knowledge, skills, and practical advice that can lead to successful scientific publications.
View Article and Find Full Text PDF

Download full-text PDF

Source
November 2008

Computer-based decision support for critical and emergency care.

J Biomed Inform 2008 Jun 26;41(3):409-12. Epub 2008 Apr 26.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2008.04.006DOI Listing
June 2008

ECTracker--an efficient algorithm for haplotype analysis and classification.

Stud Health Technol Inform 2007 ;129(Pt 2):1270-4

School of Computing, National University of Singapore, and Department of Pediatrics, National University Hospital.

This work aims at discovering the genetic variations of hemophilia A patients through examining the combination of molecular haplotypes present in hemophilia A and normal local populations using data mining methods. Data mining methods that are capable of extracting understandable and expressive patterns and also capable of making predictions based on inferences made on the patterns were explored in this work. An algorithm known as ECTracker is proposed and its performance compared with some common data mining methods such as artificial neural network, support vector machine, naive Bayesian, and decision tree (C4.5). Experimental studies and analyses show that ECTracker has comparatively good predictive accuracies in classification when compared to methods that can only perform classification. At the same time, ECTracker is also capable of producing easily comprehensible and expressive patterns for analytical purposes by experts.
View Article and Find Full Text PDF

Download full-text PDF

Source
November 2007

Predicting coronary artery disease with medical profile and gene polymorphisms data.

Stud Health Technol Inform 2007 ;129(Pt 2):1219-24

Medical Computing Laboratory, School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543.

Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees. Our experiments show that all these classifiers are comparable in terms of accuracy, while Bayesian networks have the additional advantage of being able to provide insights into the relationships among the variables. We observe that the learned Bayesian Networks identify many important dependency relationships among genetic variables, which can be verified with domain knowledge. Conforming to current domain understanding, our results indicate that related diseases (e.g., diabetes and hypertension), age and smoking status are the most important factors for CAD prediction, while the genetic polymorphisms entail more complicated influences.
View Article and Find Full Text PDF

Download full-text PDF

Source
November 2007

Biomedical knowledge discovery with topological constraints modeling in Bayesian networks: a preliminary report.

Stud Health Technol Inform 2007 ;129(Pt 1):560-5

School of Computing, National University of Singapore, Singapore.

Serving as exploratory data analysis tools, Bayesian networks (BNs) can be automatically learned from data to compactly model direct dependency relationships among the variables in a domain. A major challenge in BN learning is to effectively represent and incorporate domain knowledge in the learning process to improve its efficiency and accuracy. In this paper, we examine two types of domain knowledge representation in BNs: matrix and rule. We develop a set of consistency checking mechanisms for the representations and describe their applications in BN learning. Empirical results from the canonical Asia network example show that topological constraints, especially those imposed on the undirected links in the corresponding completed partially directed acyclic graph (CPDAG) of the learned BN, are particularly useful. Preliminary experiments on a real-life coronary artery disease dataset show that both efficiency and accuracy can be improved with the proposed methodology. The bootstrap approach adopted in the BN learning process with topological constraints also highlights the set of the learned links with high significance, which can in turn prompt further exploration of the actual relationships involved.
View Article and Find Full Text PDF

Download full-text PDF

Source
November 2007

Hybrid outcome prediction model for severe traumatic brain injury.

J Neurotrauma 2007 Jan;24(1):136-46

Acute Brain Injury Research Laboratory, Department of Neurosurgery, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433 Singapore.

Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis. This study targets the development of a set of integrated prognostication model combining different classes of outcome and prognostic factors. Methodologies such as discriminant analysis, logistic regression, decision tree, Bayesian network, and neural network were employed in the study. Several prognostication models were developed using prospectively collected data from 513 severe closed head-injured patients admitted to the Neurocritical Unit at National Neuroscience Institute of Singapore, from April 1999 to February 2003. The correlation between prognostic factors at admission and outcome at 6 months following injury was studied. Overfitting error, which may falsely distinguish different outcomes, was compared graphically. Tenfold cross-validation technique, which reduces overfitting error, was used to validate outcome prediction accuracy. The overall prediction accuracy achieved ranged from 49.79% to 81.49%. Consistently high outcome prediction accuracy was seen with logistic regression and decision tree. Combining both logistic regression and decision tree models, a hybrid prediction model was then developed. This hybrid model would more accurately predict the 6-month post-severe head injury outcome using baseline admission parameters.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1089/neu.2006.0113DOI Listing
January 2007

Set-based Cascading Approaches for Magnetic Resonance (MR) Image Segmentation (SCAMIS).

AMIA Annu Symp Proc 2006 :504-8

Department of Computer Science, School of Computing, National University of Singapore.

This paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking. In our methodology, we describe how set operations can be utilized to better overcome these problems. To evaluate the effectiveness of this approach, Magnetic Resonance Images taken from a teaching hospital research programme have been utilised, to reflect the real world quality needed for testing in patient datasets. A comparison between the pipeline and set-based methodology is also presented.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839603PMC
September 2007

Patient-specific inference and situation-dependent classification using Context-Sensitive Networks.

AMIA Annu Symp Proc 2006 :404-8

Medical Computing Lab, School of Computing, National University of Singapore, Singapore.

Representations and inferences that capture a formal notion of "context" are needed to effectively support various analytic and learning tasks in many biomedical applications. In this paper, we formulate patient-specific inference and situation-dependent classification as context-aware reasoning tasks that can be effectively supported in probabilistic graphical networks. We introduce a new probabilistic graphical framework of Context Sensitive Networks (CSNs) to efficiently represent and reason with context-sensitive knowledge. We illustrate how different types of inference in these networks can be handled in a context-dependent manner. We also demonstrate some promising evaluation results based on a set of real-life risk prediction and model classification problems in coronary heart disease.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839436PMC
September 2007

PGMC: a framework for probabilistic graphic model combination.

AMIA Annu Symp Proc 2005 :370-4

Medical Computing Laboratory, National University of Singapore.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1560518PMC
February 2007

Feature selection for the prediction of translation initiation sites.

Genomics Proteomics Bioinformatics 2005 May;3(2):73-83

Medical Computing Laboratory, School of Computing, National University of Singapore.

Translation initiation sites (TISs) are important signals in cDNA sequences. In many previous attempts to predict TISs in cDNA sequences, three major factors affect the prediction performance: the nature of the cDNA sequence sets, the relevant features selected, and the classification methods used. In this paper, we examine different approaches to select and integrate relevant features for TIS prediction. The top selected significant features include the features from the position weight matrix and the propensity matrix, the number of nucleotide C in the sequence downstream ATG, the number of downstream stop codons, the number of upstream ATGs, and the number of some amino acids, such as amino acids A and D. With the numerical data generated from these features, different classification methods, including decision tree, naïve Bayes, and support vector machine, were applied to three independent sequence sets. The identified significant features were found to be biologically meaningful, while the experiments showed promising results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5172590PMC
http://dx.doi.org/10.1016/s1672-0229(05)03012-3DOI Listing
May 2005

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

Stud Health Technol Inform 2004 ;107(Pt 2):1246-50

Medical Computing Laboratory, School of Computing, National University of Singapore, 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
June 2005

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

Stud Health Technol Inform 2004 ;107(Pt 1):104-10

School of Computing, National University of Singapore, 3 Science Drive, Singapore 117543, Singapore.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
June 2005

Automated knowledge extraction for decision model construction: a data mining approach.

AMIA Annu Symp Proc 2003 :758-62

School of Computing, National University of Singapore, 117543.

Combinations of Medical Subject Headings (MeSH) and Subheadings in MEDLINE citations may be used to infer relationships among medical concepts. To facilitate clinical decision model construction, we propose an approach to automatically extract semantic relations among medical terms from MEDLINE citations. We use the Apriori association rule mining algorithm to generate the co-occurrences of medical concepts, which are then filtered through a set of predefined semantic templates to instantiate useful relations. From such semantic relations, decision elements and possible relationships among them may be derived for clinical decision model construction. To evaluate the proposed method, we have conducted a case study in colorectal cancer management; preliminary results have shown that useful causal relations and decision alternatives can be extracted.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479908PMC
December 2004

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

Artif Intell Med 2003 Feb;27(2):201-22

Department of Computer Science, School of Computing, National University of Singapore, 3 Science Drive 2, 117543, Singapore, Singapore.

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/s0933-3657(02)00114-8DOI Listing
February 2003