Alex A T Bui

Roche Institute of Molecular Biology, Nutley, New Jersey, United States

Are you Alex A T Bui?

Claim your profile

Publications (81)73.88 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Remote ischemic conditioning (RIC) is a phenomenon in which short periods of nonfatal ischemia in 1 tissue confers protection to distant tissues. Here we performed a longitudinal human pilot study in patients with aneurysmal subarachnoid hemorrhage undergoing RIC by limb ischemia to compare changes in DNA methylation and transcriptome profiles before and after RIC. Thirteen patients underwent 4 RIC sessions over 2 to 12 days after rupture of an intracranial aneurysm. We analyzed whole blood transcriptomes using RNA sequencing and genome-wide DNA methylomes using reduced representation bisulfite sequencing, both before and after RIC. We tested differential expression and differential methylation using an intraindividual paired study design and then overlapped the differential expression and differential methylation results for analyses of functional categories and protein-protein interactions. We observed 164 differential expression genes and 3493 differential methylation CpG sites after RIC, of which 204 CpG sites overlapped with 103 genes, enriched for pathways of cell cycle (P<3.8×10(-4)) and inflammatory responses (P<1.4×10(-4)). The cell cycle pathway genes form a significant protein-protein interaction network of tightly coexpressed genes (P<0.00001). Gene expression and DNA methylation changes in aneurysmal subarachnoid hemorrhage patients undergoing RIC are involved in coordinated cell cycle and inflammatory responses. © 2015 American Heart Association, Inc.
    Stroke 08/2015; 46(9). DOI:10.1161/STROKEAHA.115.009618 · 5.72 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes the information retrieval step in Casama (Contextualized Semantic Maps), a project that summarizes and contextualizes current research papers on driver mutations in non-small cell lung cancer. Casama׳s representation of lung cancer studies aims to capture elements that will assist an end-user in retrieving studies and, importantly, judging their strength. This paper focuses on two types of study metadata: study objective and study design. 430 abstracts on EGFR and ALK mutations in lung cancer were annotated manually. Casama׳s support vector machine (SVM) automatically classified the abstracts by study objective with as much as 129% higher F-scores compared to PubMed׳s built-in filters. A second SVM classified the abstracts by epidemiological study design, suggesting strength of evidence at a more granular level than in previous work. The classification results and the top features determined by the classifiers suggest that this scheme would be generalizable to other mutations in lung cancer, as well as studies on driver mutations in other cancer domains. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Computers in Biology and Medicine 01/2015; 58C:63-72. DOI:10.1016/j.compbiomed.2015.01.004 · 1.24 Impact Factor
  • Source

    Energy Minimization Methods in Computer Vision and Pattern Recognition; 01/2015
  • Source
    Shiwen Shen · Alex A.T. Bui · Jason Cong · William Hsu ·
    [Show abstract] [Hide abstract]
    ABSTRACT: Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists’ diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the Lung Imaging Database Consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on ten randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert.
    Computers in Biology and Medicine 12/2014; 57. DOI:10.1016/j.compbiomed.2014.12.008 · 1.24 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Patient portals have the potential to provide content that is specifically tailored to a patient's information needs based on diagnoses and other factors. In this work, we conducted a survey of 41 lung cancer patients at an outpatient lung cancer clinic at the medical center of the University of California, Los Angeles, to gain insight into these perceived information needs and opinions on the design of a portal to fulfill them. We found that patients requested access to information related to diagnosis and imaging, with more than half of the patients reporting that they did not anticipate an increase in anxiety due to access to medical record information via a portal. We also found that patient educational background did not lead to a significant difference in desires for explanations of reports and definitions of terms.
    Journal of the Association for Information Science and Technology 08/2014; 66(8). DOI:10.1002/asi.23269 · 2.23 Impact Factor
  • Kyle W Singleton · Alex A T Bui · William Hsu ·

    Journal of the American Medical Informatics Association 07/2014; 21(E2). DOI:10.1136/amiajnl-2014-002968 · 3.50 Impact Factor
  • Source

    International Stroke Conference 2014; 02/2014
  • [Show abstract] [Hide abstract]
    ABSTRACT: Practitioner guidelines simultaneously provide broad overviews and in-depth details of disease. Written for experts, they are difficult for patients to understand, yet patients often use these guidelines as a source of information to help them to learn about their health. Using practitioner guidelines along with patient information needs and preferences, we created a method to design an information model for providing patients access to their personal health information, linked to individualized, relevant supporting information from guidelines within a patient portal. This model consists of twelve classes of concepts. We manually reviewed and annotated medical records to demonstrate the validity of our model. Each class of the model was found within at least one patient's record, and seven classes of concepts appeared in over half of the patients' records annotated. These annotations show that the model produced by the method can be used to determine what guideline information is relevant to an individual patient, based on concepts in their health information.
    AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2014; 2014:1835-44.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model to predict stroke mortality at discharge. Finally, we discuss how the identification of a reduced feature set allowed us to identify additional cases in our research database for validation testing. Our classifier achieved a c-statistic of 0.865 on the cross-validated dataset, demonstrating good classification performance using a reduced set of variables.
    AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2014; 2014:1787-96.
  • [Show abstract] [Hide abstract]
    ABSTRACT: With the large number of clinical practice guidelines available, there is an increasing need for a comprehensive unified model for acute ischemic stroke treatment to assist in clinical decision making. We present a unified treatment model derived through review of existing clinical practice guidelines, meta-analyses, and clinical trials. Using logic from the treatment model, a Bayesian belief network was defined and fitted to data from our institution's observational quality improvement database for acute stroke patients. The resulting network validates known relationships between variables, treatment decisions and outcomes, and enables the exploration of new correlative relationships not defined in current guidelines.
    Studies in health technology and informatics 08/2013; 192(1):1012. DOI:10.3233/978-1-61499-289-9-1012
  • Ming Yan · Alex A.T. Bui · Jason Cong · Luminita A. Vese ·
    [Show abstract] [Hide abstract]
    ABSTRACT: Obtaining high quality images is very important in many areas of applied sciences, such as medical imaging, optical microscopy, and astronomy. Image reconstruction can be considered as solving the ill-posed and inverse problem y=Ax+n, where x is the image to be reconstructed and n is the unknown noise. In this paper, we propose general robust expectation maximization (EM)-type algorithms for image reconstruction. Both Poisson noise and Gaussian noise types are considered. The EM-type algorithms are performed using iteratively EM (or SART for weighted Gaussian noise) and regularization in the image domain. The convergence of these algorithms is proved in several ways: EM with a priori information and alternating minimization methods. To show the efficiency of EM-type algorithms, the application in computerized tomography reconstruction is chosen.
    Inverse Problems and Imaging 08/2013; 3(3). DOI:10.3934/ipi.2013.7.1007 · 1.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving-from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.
    Journal of the American Medical Informatics Association 06/2013; 20(6). DOI:10.1136/amiajnl-2012-001340 · 3.50 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective: With the increased routine use of advanced imaging in clinical diagnosis and treatment, it has become imperative to provide patients with a means to view and understand their imaging studies. We illustrate the feasibility of a patient portal that automatically structures and integrates radiology reports with corresponding imaging studies according to several information orientations tailored for the layperson. Methods: The imaging patient portal is composed of an image processing module for the creation of a timeline that illustrates the progression of disease, a natural language processing module to extract salient concepts from radiology reports (73% accuracy, F1 score of 0.67), and an interactive user interface navigable by an imaging findings list. The portal was developed as a Java-based web application and is demonstrated for patients with brain cancer. Results and discussion: The system was exhibited at an international radiology conference to solicit feedback from a diverse group of healthcare professionals. There was wide support for educating patients about their imaging studies, and an appreciation for the informatics tools used to simplify images and reports for consumer interpretation. Primary concerns included the possibility of patients misunderstanding their results, as well as worries regarding accidental improper disclosure of medical information. Conclusions: Radiologic imaging composes a significant amount of the evidence used to make diagnostic and treatment decisions, yet there are few tools for explaining this information to patients. The proposed radiology patient portal provides a framework for organizing radiologic results into several information orientations to support patient education.
    Journal of the American Medical Informatics Association 06/2013; 20(6). DOI:10.1136/amiajnl-2012-001457 · 3.50 Impact Factor
  • William Hsu · Alex A T Bui ·
    [Show abstract] [Hide abstract]
    ABSTRACT: Observational patient data provides an unprecedented opportunity to gleam new insights into diseases and assess patient quality of care, but a challenge lies in matching our ability to collect data with a comparable ability to understand and apply this information. Visual analytic techniques are promising as they permit the exploration and manipulation of complex datasets through a graphical user interface. Nevertheless, current visualization tools rely on users to manually configure which aspects of the dataset are shown and how they are presented. In this paper, we describe an approach that utilizes characteristics of the data and domain knowledge to assist users with summarizing the information space of a large population. We present a representation that captures contextual information about the data and constructs that operate on this information to tailor the data's presentation. We describe a use case of this approach in exploring a claims dataset of individuals with spinal dysraphism.
    AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2013; 2013:615-23.
  • [Show abstract] [Hide abstract]
    ABSTRACT: AIMS. NIDA/UCLA Quit Using Drugs Intervention Trial (QUIT) conducted an RCT of a very brief intervention for reducing risky drug use and harm in low-income, diverse primary care patients in FQHCs. The design emphasizes screening, very brief clinician advice (2-3 minutes), and two telephone drug-use health education sessions vs usual care control (240 per condition).We present findings on recruitment in a Skid Row FQHC. METHODS. Pre-visit screening of adults was conducted using a touchscreen Tablet PC. At-risk drug use was defined as casual, frequent, or binge use without dependence (ASSIST score 4-26). RESULTS. In 2011, 1060 adults were approached: 86% 40+ yo; 70% male; 64% Black, 21% Latino, 13% white; 70% homeless. 80% were excluded prior to the ASSIST. Among the 210 who completed the ASSIST, 23% were dependent on drugs or alcohol. ASSIST scores were (no/low risk, moderate risk, dependence, respectively): tobacco (24, 48, 28), alcohol (28, 46, 26), cannabis (43, 36, 21), cocaine (42, 34, 24), opioids (60, 26, 14), sedatives (66, 22, 12), methamphetamine/ amphetamine type stimulants (69, 20, 11), hallucinogens (81, 14, 5), inhalants (86, 10, 4). Excluding low risk- or dependent users, 56 patients (5.3% of those observed in waiting room) met study criteria of past 3 mo risky stimulant use. CONCLUSIONS. Integrating SBIRT into FQHCs is feasible. In Skid Row, only 5% of patients qualified for the study based on rates of risky stimulant use, as eligible patients were generally low- or dependent users. Stimulant rates observed are higher than in general populations (NSDUH).
    140st APHA Annual Meeting and Exposition 2012; 10/2012
  • Juan Anna Wu · William Hsu · Alex A.T. Bui ·
    [Show abstract] [Hide abstract]
    ABSTRACT: With the increasing amount of information collected through clinical practice and scientific experimentation, a growing challenge is how to utilize available resources to construct predictive models to facilitate clinical decision making. Clinicians often have questions related to the treatment and outcome of a medical problem for individual patients; however, few tools exist that leverage the large collection of patient data and scientific knowledge to answer these questions. Without appropriate context, existing data that have been collected for a specific task may not be suitable for creating new models that answer different questions. This paper presents an approach that leverages available structured or unstructured data to build a probabilistic predictive model that assists physicians with answering clinical questions on individual patients. Various challenges related to transforming available data to an end-user application are addressed: problem decomposition, variable selection, context representation, automated extraction of information from unstructured data sources, model generation, and development of an intuitive application to query the model and present the results. We describe our efforts towards building a model that predicts the risk of vasospasm in aneurysm patients.
    Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on; 09/2012
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The advent of remote and wearable medical sensing has created a dire need for efficient medical time series databases. Wearable medical sensing devices provide continuous patient monitoring by various types of sensors and have the potential to create massive amounts of data. Therefore, time series databases must utilize highly optimized indexes in order to efficiently search and analyze stored data. This paper presents a highly efficient technique for indexing medical time series signals using Locality Sensitive Hashing (LSH). Unlike previous work, only salient (or interesting) segments are inserted into the index. This technique reduces search times by up to 95% while yielding near identical search results.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:5086-9. DOI:10.1109/EMBC.2012.6347137
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Diabetes is the seventh leading cause of death in the United States. In 2010, about 1.9 million new cases of diabetes were diagnosed in people aged 20 years or older. Remote health monitoring systems can help diabetics and their healthcare professionals monitor health-related measurements by providing real-time feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the remote health monitoring. This paper presents a task optimization technique used in WANDA (Weight and Activity with Blood Pressure and Other Vital Signs); a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. WANDA applies data analytics in real-time to improving the quality of care. The developed algorithm minimizes the number of daily tasks required by diabetic patients using association rules that satisfies a minimum support threshold. Each of these tasks maximizes information gain, thereby improving the overall level of care. Experimental results show that the developed algorithm can reduce the number of tasks up to 28.6% with minimum support 0.95, minimum confidence 0.97 and high efficiency.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:2223-6. DOI:10.1109/EMBC.2012.6346404
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We believe that by adapting architectures to fit the requirements of a given application domain, we can significantly improve the efficiency of computation. To validate the idea for our application domain, we evaluate a wide spectrum of commodity computing platforms to quantify the potential benefits of heterogeneity and customization for the domain-specific applications. In particular, we choose medical imaging as the application domain for investigation, and study the application performance and energy efficiency across a diverse set of commodity hardware platforms, such as general-purpose multi-core CPUs, massive parallel many-core GPUs, low-power mobile CPUs and fine-grain customizable FPGAs. This study leads to a number of interesting observations that can be used to guide further development of domain-specific architectures.
    05/2012; DOI:10.1109/ASPDAC.2012.6165071
  • [Show abstract] [Hide abstract]
    ABSTRACT: Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naïve Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.
    IEEE Transactions on Biomedical Circuits and Systems 04/2012; 6(2):167-178. DOI:10.1109/TBCAS.2011.2166073 · 2.48 Impact Factor

Publication Stats

559 Citations
73.88 Total Impact Points


  • 2015
    • Roche Institute of Molecular Biology
      Nutley, New Jersey, United States
  • 2002-2015
    • University of California, Los Angeles
      • • Department of Bioengineering
      • • Department of Radiology
      • • Department of Electrical Engineering
      Los Ángeles, California, United States
  • 2008
    • Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center
      Torrance, California, United States
  • 1998
    • University of California, Davis
      Davis, California, United States