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Articles 91 - 110 of 110
Full-Text Articles in Health Information Technology
Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong
Fusing Heterogeneous Data For Alzheimer's Disease Classification, P. S. Pillai, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
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 …
Physio@Home: Exploring Visual Guidance And Feedback Techniques For Physiotherapy Exercises, Richard Tang, Xing-Dong Yang, Scott Bateman, Joaquim Jorge, Anthony Tang
Physio@Home: Exploring Visual Guidance And Feedback Techniques For Physiotherapy Exercises, Richard Tang, Xing-Dong Yang, Scott Bateman, Joaquim Jorge, Anthony Tang
Research Collection School Of Computing and Information Systems
Physiotherapy patients exercising at home alone are at risk of re-injury since they do not have corrective guidance from a therapist. To explore solutions to this problem, we designed Physio@Home, a prototype that guides people through pre-recorded physiotherapy exercises using realtime visual guides and multi-camera views. Our design addresses several aspects of corrective guidance, including: plane and range of movement, joint positions and angles, and extent of movement. We evaluated our design, comparing how closely people could follow exercise movements under various feedback conditions. Participants were most accurate when using our visual guide and multi-views. We provide suggestions for exercise …
Automated Prediction Of Glasgow Outcome Scale For Traumatic Brain Injury, Bolan Su, Thien Anh Dinh, A. K. Ambastha, Tianxia Gong, Tomi Silander, Shijian Lu, C. C. Tchoyoson Lim, Boon Chuan Pang, Cheng Kiang Lee, Tze-Yun Leong, Chew Lim Tan
Automated Prediction Of Glasgow Outcome Scale For Traumatic Brain Injury, Bolan Su, Thien Anh Dinh, A. K. Ambastha, Tianxia Gong, Tomi Silander, Shijian Lu, C. C. Tchoyoson Lim, Boon Chuan Pang, Cheng Kiang Lee, Tze-Yun Leong, Chew Lim Tan
Research Collection School Of Computing and Information Systems
Clinical features found in brain CT scan images are widely used in traumatic brain injury (TBI) as indicators for Glasgow Outcome Scale (GOS) prediction. However, due to the lack of automated methods to measure and quantify the CT scan image features, the computerized prediction of GOS in TBI has not been well studied. This paper introduces an automated GOS prediction system for traumatic brain CT images. Different from most existing systems that perform the prognosis based on pre-processed data, our system directly works on brain CT scan images based on the image features. Our system can also be extended to …
Medical Imaging Specialists And 3d: A Domain Perspective On Mobile 3d Interactions, Teddy Seyed, Frank Maurer, Francisco Marinho Rodrigues, Anthony Tang
Medical Imaging Specialists And 3d: A Domain Perspective On Mobile 3d Interactions, Teddy Seyed, Frank Maurer, Francisco Marinho Rodrigues, Anthony Tang
Research Collection School Of Computing and Information Systems
3D volumetric medical images, such as MRIs, are commonly explored and interacted with by medical imaging experts using systems that require keyboard and mouse-based techniques. These techniques have presented challenges for medical imaging specialists: 3D spatial navigation is difficult, in addition to the detailed selection and analysis of 3D medical images being difficult due to depth perception and occlusion issues. In this work, we explore a potential solution to these challenges by using tangible interaction techniques with a mobile device to simplify 3D interactions for medical imaging specialists. We discuss preliminary observations from our design sessions with medical imaging specialists …
An Automated Pathological Class Level Annotation System For Volumetric Brain Images, Thien Anh Dinh, Tomi Silander, C. C. Tchoyoson Lim, Tze-Yun Leong
An Automated Pathological Class Level Annotation System For Volumetric Brain Images, Thien Anh Dinh, Tomi Silander, C. C. Tchoyoson Lim, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
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 …
Discussion Of "Biomedical Ontologies: Toward Scientific Debate", Brochhausen M., Burgun A., Ceusters W., Hasman A., Tze-Yun Leong, Musen M., Oliveira J., Peleg M., Rector A., Schulz S.
Discussion Of "Biomedical Ontologies: Toward Scientific Debate", Brochhausen M., Burgun A., Ceusters W., Hasman A., Tze-Yun Leong, Musen M., Oliveira J., Peleg M., Rector A., Schulz S.
Research Collection School Of Computing and Information Systems
With these comments on the paper “Biomedical Ontologies: Toward scientific debate”, written by Victor Maojo et al., Methods of Information in Medicine wants to stimulate a discussion on advantages and challenges of biomedical ontologies. An international group of experts have been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.
Medially: A Provenance-Aware Remote Health Monitoring Middleware, Atanu Roy Chowdhury, Ben Falchuk, Archan Misra
Medially: A Provenance-Aware Remote Health Monitoring Middleware, Atanu Roy Chowdhury, Ben Falchuk, Archan Misra
Research Collection School Of Computing and Information Systems
This paper presents MediAlly, a middleware for supporting energy-efficient, long-term remote health monitoring. Data is collected using physiological sensors and transported back to the middleware using a smart phone. The key to MediAlly's energy efficient operations lies in the adoption of an Activity Triggered Deep Monitoring (ATDM) paradigm, where data collection episodes are triggered only when the subject is determined to possess a specified context. MediAlly supports the on-demand collection of contextual provenance using a novel low-overhead provenance collection sub-system. The behaviour of this sub-system is configured using an application-defined context composition graph. The resulting provenance stream provides valuable insight …
Teleoph: A Secure Real-Time Teleophthalmology System, Yongdong Wu, Zhou Wei, Haixia Yao, Zhigang Zhao, Lek Heng Ngoh, Robert H. Deng, Shengsheng Yu
Teleoph: A Secure Real-Time Teleophthalmology System, Yongdong Wu, Zhou Wei, Haixia Yao, Zhigang Zhao, Lek Heng Ngoh, Robert H. Deng, Shengsheng Yu
Research Collection School Of Computing and Information Systems
Teleophthalmology (TeleOph) is an electronic counterpart of today's face-to-face, patient-to-specialist ophthalmology system. It enables one or more ophthalmologists to remotely examine a patient's condition via a confidential and authentic communication channel. Specifically, TeleOph allows a trained nonspecialist in a primary clinic to screen the patients with digital instruments (e.g., camera, ophthalmoscope). The acquired medical data are delivered to the hospital where an ophthalmologist will review the data collected and, if required, provide further consultation for the patient through a real-time secure channel established over a public Internet network. If necessary, the ophthalmologist is able to further sample the images/video of …
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Research Collection School Of Computing and Information Systems
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. …
A Time-And-Value Centric Provenance Model And Architecture For Medical Event Streams, Marion Bllount, John Davis, Archan Misra, Daby Sow, Min Wang
A Time-And-Value Centric Provenance Model And Architecture For Medical Event Streams, Marion Bllount, John Davis, Archan Misra, Daby Sow, Min Wang
Research Collection School Of Computing and Information Systems
Provenance becomes a critical requirement for healthcare IT infrastructures, especially when pervasive biomedical sensors act as a source of raw medical streams for large-scale, automated clinical decision support systems. Medical and legal requirements will make it obligatory for such systems to answer queries regarding the underlying data samples from which output alerts are derived, the IDs of the processing components used and the privileges of the individuals and software components accessing the medical data. Unfortunately, existing models of either annotation or process based provenance are designed for transaction-oriented systems and do not satisfy the unique requirements for systems processing high-volume, …
Set-Based Cascading Approaches For Magnetic Resonance (Mr) Image Segmentation (Scamis), Jiang Liu, Tze-Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang
Set-Based Cascading Approaches For Magnetic Resonance (Mr) Image Segmentation (Scamis), Jiang Liu, Tze-Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang
Research Collection School Of Computing and Information Systems
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 …
Mobile Healthcare Informatics, Keng Siau, Zixing Shen
Mobile Healthcare Informatics, Keng Siau, Zixing Shen
Research Collection School Of Computing and Information Systems
Advances in wireless technology give pace to the rapid development of mobile applications. The coming mobile revolution will bring dramatic and fundamental changes to our daily life. It will influence the way we live, the way we do things, and the way we take care of our health. For the healthcare industry, mobile applications provide a new frontier in offering better care and services to patients, and a more flexible and mobile way of communicating with suppliers and patients. Mobile applications will provide important real time data for patients, physicians, insurers, and suppliers. In addition, it will revolutionalize the way …
Health Care Informatics, Keng Siau
Health Care Informatics, Keng Siau
Research Collection School Of Computing and Information Systems
The health care industry is currently experiencing a fundamental change. Health care organizations are reorganizing their processes to reduce costs, be more competitive, and provide better and more personalized customer care. This new business strategy requires health care organizations to implement new technologies, such as Internet applications, enterprise systems, and mobile technologies in order to achieve their desired business changes. This article offers a conceptual model for implementing new information systems, integrating internal data, and linking suppliers and patients.
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Research Collection School Of Computing and Information Systems
With the huge amount of data collected by scientists in the molecular genetics community in recent years, there exists a need to develop some novel algorithms based on existing data mining techniques to discover useful information from genome databases. We propose an algorithm that integrates the statistical method, association rule mining, and classification rule mining in the discovery of allelic combinations of genes that are peculiar to certain phenotypes of diseased patients.
Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong
Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is …
Decision Support Methods In Diabetic Patient Management By Insulin Administration Neural Network Vs. Induction Methods For Knowledge Classification, B. V. Ambrosiadou, S. Vadera, Venky Shankaraman, D. Goulis, G. Gogou
Decision Support Methods In Diabetic Patient Management By Insulin Administration Neural Network Vs. Induction Methods For Knowledge Classification, B. V. Ambrosiadou, S. Vadera, Venky Shankaraman, D. Goulis, G. Gogou
Research Collection School Of Computing and Information Systems
Diabetes mellitus is now recognised as a major worldwide public health problem. At present, about 100 million people are registered as diabetic patients. Many clinical, social and economic problems occur as a consequence of insulin-dependent diabetes. Treatment attempts to prevent or delay complications by applying ‘optimal’ glycaemic control. Therefore, there is a continuous need for effective monitoring of the patient. Given the popularity of decision tree learning algorithms as well as neural networks for knowledge classification which is further used for decision support, this paper examines their relative merits by applying one algorithm from each family on a medical problem; …
Information Technology In The Health Care Industry: A Primer, P. Southard, S. J. Hong, Keng Siau
Information Technology In The Health Care Industry: A Primer, P. Southard, S. J. Hong, Keng Siau
Research Collection School Of Computing and Information Systems
The paper discusses current and future applications of information technology within the healthcareindustry. It presents some broad strategies for approaching information technology investments and various tools available.
Decision Analytic Approach To Severe Head Injury Management., Harmanec D., Tze-Yun Leong, Sundaresh S., Poh K., Yeo T., Ng I., Lew T.
Decision Analytic Approach To Severe Head Injury Management., Harmanec D., Tze-Yun Leong, Sundaresh S., Poh K., Yeo T., Ng I., Lew T.
Research Collection School Of Computing and Information Systems
Severe head injury management in the intensive care unit is extremely challenging due to the complex domain, the uncertain intervention efficacies, and the time-critical setting. We adopt a decision analytic approach to automate the management process. We document our experience in building a simplified influence diagram that involves about 3000 numerical parameters. We identify the inherent problems in structuring a model with unclear domain relationships, numerous interacting variables, and real-time multiple inputs. We analyze the effectiveness and limitations of the decision analytic approach and present a set of desiderata for effective knowledge acquisition in this setting. We also propose a …
Modelling Medical Decisions In Dynamol: A New General Framework Of Dynamic Decision Analysis, Tze-Yun Leong, Cungen Cao
Modelling Medical Decisions In Dynamol: A New General Framework Of Dynamic Decision Analysis, Tze-Yun Leong, Cungen Cao
Research Collection School Of Computing and Information Systems
Dynamic decision analysis concerns decision problems in which both time and uncertainty are explicitly considered. We present a new dynamic decision analysis framework, called DynamoL, that supports graphical presentation of the decision factors in multiple perspectives. To alleviate the difficulty in assessing conditional probabilities over time in dynamic decision models, DynaMoL incorporates a Bayesian learning system to automatically learn the probabilistic parameters from large medical databases. We describe the DynaMoL modeling and learning architecture through a medical decision problem on the optimal follow-up schedule for patients after curative colorectal cancer surgery. We also show that the modeling experience and results …
Dynamic Decision Modeling In Medicine: A Critique Of Existing Formalisms, Tze-Yun Leong
Dynamic Decision Modeling In Medicine: A Critique Of Existing Formalisms, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
Dynamic decision models are frameworks for modeling and solving decision problems that take into explicit account the effects of time. These formalisms are based on structural and semantical extensions of conventional decision models, e.g., decision trees and influence diagrams, with the mathematical definitions of finite-state semi-Markov processes. This paper identifies the common theoretical basis of existing dynamic decision modeling formalisms, and compares and contrasts their applicability and efficiency. It also argues that a subclass of such dynamic decision problems can be formulated and solved more effectively with non-graphical techniques. Some insights gained from this exercise on automating the dynamic decision …