Biomedical subjects

Alex A T Bui

Publications and source records attributed to Alex A T Bui.

5 recordsLinked to original sources

PubMed2002

Evidence-based radiology: requirements for electronic access.

RATIONALE AND OBJECTIVES: The purpose of this study was to determine the electronic requirements for supporting evidence-based radiology in today's medical environment. MATERIALS AND METHODS: A software engineering technique, use case modeling, was performed for several clinical settings to determine the use of imaging and its role in evidence-based practice, with particular attention to issues relating to data access and the usage of clinical information. From this basic understanding, the analysis was extended to encompass evidence-based radiologic research and teaching. RESULTS: The analysis showed that a system supporting evidence-based radiology must (a) provide a single point of access to multiple clinical data sources so that patient data can be readily used and incorporated into comprehensive radiologic consults and (b) provide quick access to external evidence in the way of similar patient cases and published medical literature, thus supporting evidence-based practice. CONCLUSION: Information infrastructures that aim to support evidence-based radiology not only must address issues related to the integration of clinical data from heterogeneous databases, but must facilitate access and filtering of patient data in order to improve radiologic consultation.

Evidence-Based Medicine↗

PubMed2002

DataServer: an infrastructure to support evidence-based radiology.

Following a requirements analysis for development of an information infrastructure supporting evidence-based radiology, the objective of this study was the development of a data gateway to support flexible access to the totality of a patient's electronic medical records through a single, uniform representation, regardless of the underlying data sources (eg, hospital information systems [HIS], radiology information systems [RIS], picture archiving and communication systems [PACS]). XML-based (eXtensible Markup Language) technologies were employed to create an application framework permitting querying of different clinical databases. The contents of different data sources were represented by using XML. On the basis of these representations, users can specify queries. The system transforms the XML queries into a query format understood by the specific databases, processes the query, and transforms the results back into an XML format. XML results can then be transformed in accordance to different data-formatting standards. Access to several different data sources, including HIS, RIS, and PACS, has been accomplished with this framework. The extensible nature of the XML data gateway enables data sources to be readily added. The framework also provides a means by which data can be systematically de-identified to protect patient confidentiality, thus supporting research endeavors.

Evidence-Based Medicine↗

PubMed2002

An XML Gateway to Patient Data for Medical Research Applications.

As the medical environment becomes increasingly electronic, clinical databases are continually growing, accruing masses of patient information. This wealth of data is an invaluable source of information to researchers, serving as a testbed for the development of new information technologies and as a repository of real-world data for data mining and population-based studies. However, the true utility of this information is not fulfilled, in part because of issues pertaining to security and patient confidentiality, but also due to the lack of an effective infrastructure to access the data. This paper describes a system, DataServer, that permits researchers to query and retrieve data from multiple clinical data sources, automatically deidentifying patient data so that it can be used for research purposes. DataServer functions as an application framework, enabling extensible markup language (XML)-based querying of existing medical databases. Key aspects of DataServer include ready inclusion of new information resources, minimal processing impact on existing clinical systems via a distributed cache, and flexible output representation via XSL (eXtensible Style Language) transforms.

Databases, Factual↗

PubMed2002

A context-sensitive methodology for automatic episode creation.

Episode creation, the task of classifying medical events and related clinical data to a high-level concept, such as a disease, illness or care, has been primarily an interest of healthcare payers for purposes of cost outcomes analysis. Traditional challenges in episode creation have included: inconsistencies in defining episodes; lack of sufficient information to infer episodes; and differences in methods for diagnosing and resolving episodes. However, with the advent of the electronic medical record, which contains multiple sources of patient-related information, data is now accessible to construct more accurate and refined episodes. This work presents a context-sensitive episode creation methodology that utilizes features extracted from different medical repositories (e.g., claims records, structured medical reports) to associate the data with their respective motivating episodes. The combinatorial approach used to find the optimal clustering of patient-related data into episode groups and the measure used to evaluate candidate episode sets are described.

Episode of Care↗

PubMed2002

Identification of patient name references within medical documents using semantic selectional restrictions.

De-identification of a patient's personal data from medical records is a protective legal requirement imposed before medical documents can be used for research purposes or transferred to other healthcare providers (e.g., teachers, students, tele-consultations). This de-identification process is tedious if performed manually, and is known to be quite faulty in direct search and replace strategies [9]. In this paper, we report on the identification step of this process. The proposed algorithm is based on estimating the fitness of candidate patient name references to a set of semantic selectional restrictions. The semantic restrictions place tight contextual requirements upon candidate words in the report text and are determined automatically from a manually tagged corpus of training reports. Maximum entropy classifiers are used to provide a probabilistic measure of the belief of a given candidate token to a given semantic restriction. We report on the design and preliminary evaluation of the system within the do-main of pediatric urology.

Algorithms↗