Alex A T Bui

CSU Mentor, Long Beach, California, United States

Are you Alex A T Bui?

Claim your profile

Publications (66)53.14 Total impact

  • [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; · 2.23 Impact Factor
  • Kyle W Singleton, Alex A T Bui, William Hsu
    Journal of the American Medical Informatics Association : JAMIA. 07/2014;
  • Source
    International Stroke Conference 2014; 02/2014
  • [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). · 1.14 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; · 3.57 Impact Factor
  • [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; · 3.57 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: 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 01/2013; 192:1012.
  • [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
  • 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.
  • 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.
  • [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. · 2.74 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Due to the increasingly data-intensive clinical environment, physicians now have unprecedented access to detailed clinical information from a multitude of sources. However, applying this information to guide medical decisions for a specific patient case remains challenging. One issue is related to presenting information to the practitioner: displaying a large (irrelevant) amount of information often leads to information overload. Next-generation interfaces for the electronic health record (EHR) should not only make patient data easily searchable and accessible, but also synthesize fragments of evidence documented in the entire record to understand the etiology of a disease and its clinical manifestation in individual patients. In this paper, we describe our efforts toward creating a context-based EHR, which employs biomedical ontologies and (graphical) disease models as sources of domain knowledge to identify relevant parts of the record to display. We hypothesize that knowledge (e.g., variables, relationships) from these sources can be used to standardize, annotate, and contextualize information from the patient record, improving access to relevant parts of the record and informing medical decision making. To achieve this goal, we describe a framework that aggregates and extracts findings and attributes from free-text clinical reports, maps findings to concepts in available knowledge sources, and generates a tailored presentation of the record based on the information needs of the user. We have implemented this framework in a system called Adaptive EHR, demonstrating its capabilities to present and synthesize information from neurooncology patients. This paper highlights the challenges and potential applications of leveraging disease models to improve the access, integration, and interpretation of clinical patient data.
    IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 03/2012; 16(2):228-34. · 1.69 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Remote and wearable medical sensing has the potential to create very large and high dimensional datasets. Medical time series databases must be able to efficiently store, index, and mine these datasets to enable medical professionals to effectively analyze data collected from their patients. Conventional high dimensional indexing methods are a two stage process. First, a superset of the true matches is efficiently extracted from the database. Second, supersets are pruned by comparing each of their objects to the query object and rejecting any objects falling outside a predetermined radius. This pruning stage heavily dominates the computational complexity of most conventional search algorithms. Therefore, indexing algorithms can be significantly improved by reducing the amount of pruning. This paper presents an online algorithm to aggregate biomedical times series data to significantly reduce the search space (index size) without compromising the quality of search results. This algorithm is built on the observation that biomedical time series signals are composed of cyclical and often similar patterns. This algorithm takes in a stream of segments and groups them to highly concentrated collections. Locality Sensitive Hashing (LSH) is used to reduce the overall complexity of the algorithm, allowing it to run online. The output of this aggregation is used to populate an index. The proposed algorithm yields logarithmic growth of the index (with respect to the total number of objects) while keeping sensitivity and specificity simultaneously above 98%. Both memory and runtime complexities of time series search are improved when using aggregated indexes. In addition, data mining tasks, such as clustering, exhibit runtimes that are orders of magnitudes faster when run on aggregated indexes.
    Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on; 01/2012
  • 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.
    01/2012;
  • J.A. Wu, W. Hsu, A.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; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.
    Healthcare Informatics, Imaging and Systems Biology (HISB), 2012 IEEE Second International Conference on; 01/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Time series subsequence matching (or signal searching) has importance in a variety of areas in health care informatics. These areas include case-based diagnosis and treatment as well as the discovery of trends and correlations between data. Much of the traditional research in signal searching has focused on high dimensional R-NN matching. However, the results of R-NN are often small and yield minimal information gain; especially with higher dimensional data. This paper proposes a randomized Monte Carlo sampling method to broaden search criteria such that the query results are an accurate sampling of the complete result set. The proposed method is shown both theoretically and empirically to improve information gain. The number of query results are increased by several orders of magnitude over approximate exact matching schemes and fall within a Gaussian distribution. The proposed method also shows excellent performance as the majority of overhead added by sampling can be mitigated through parallelization. Experiments are run on both simulated and real-world biomedical datasets.
    Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on; 01/2012
  • E. Watt, J.W. Sayre, A.A.T. Bui
    [Show abstract] [Hide abstract]
    ABSTRACT: Dynamic Bayesian Belief networks (DBNs) have been commonly used to represent temporal data in several domains, however, an ideal representation requires a near perfect mapping between the process being modeled and the DBN. Furthermore, DBNs assume a full set of observations collected at a fixed frequency. Bayesian model selection has arisen to address biased inference and underlying assumptions about the data (e.g., distribution, representative ness) to choose a model that best fits the given observations. Per patient case, a Bayesian model is generated to maximize specificity, and the collective set of models is averaged to fit all examples. This paper demonstrates the advantages of patient-specific modeling over a DBN-driven approach. Results evaluating this approach are presented based on models for two longitudinal clinical datasets (neuro-oncology, knee osteoarthritis). Largely, the patient-specific models show improved performance in prediction relative to the DBNs.
    Healthcare Informatics, Imaging and Systems Biology (HISB), 2011 First IEEE International Conference on; 08/2011
  • Source
    W. Hsu, R.K. Taira, F. Vinuela, A.A.T. Bui
    [Show abstract] [Hide abstract]
    ABSTRACT: Electronic medical records capture large quantities of patient data generated as a result of routine care. Secondary use of this data for clinical research could provide new insights into the evolution of diseases and help assess the effectiveness of available interventions. Unfortunately, the unstructured nature of clinical data hinders a user's ability to understand this data: tools are needed to structure, model, and visualize the data to elucidate patterns in a patient population. We present a case-based retrieval framework that incorporates an extraction tool to identify concepts from clinical reports, a disease model to capture necessary context for interpreting extracted concepts, and a model-driven visualization to facilitate querying and interpretation of the results. We describe how the model is used to group, filter, and retrieve similar cases. We present an application of the framework that aids users in exploring a population of intracranial aneurysm patients.
    Healthcare Informatics, Imaging and Systems Biology (HISB), 2011 First IEEE International Conference on; 08/2011