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Biomedical subjects

Hooshang Kangarloo

Publications and source records attributed to Hooshang Kangarloo.

At least 19 recordsLinked to original sources

Automatic generation of repeated patient information for tailoring clinical notes.

Generating clear, readable, and accurate reports can be a time-consuming task for physicians. Clinical notes, which document patient encounters, often contain a certain set of patient information including demographics, medical history, surgical history, examination results or the current medical condition that is propagated from one clinical note to all subsequent clinical notes for the same patient. To this end, we present a system, which automatically generates this patient information for the creation of a new clinical note. We use semantic patterns and an approximate sequence matching algorithm for capturing the discourse role of sentences, which we show to be a useful feature for determining whether the sentence should be repeated. Our system is shown to perform better than a simple baseline metric using precision/recall results. We believe such a system would allow clinical notes to be more complete, timely, and accurate.

Documentation↗

Generating models of surgical procedures using UMLS concepts and multiple sequence alignment.

Surgical procedures can be viewed as a process composed of a sequence of steps performed on, by, or with the patient's anatomy. This sequence is typically the pattern followed by surgeons when generating surgical report narratives for documenting surgical procedures. This paper describes a methodology for semi-automatically deriving a model of conducted surgeries, utilizing a sequence of derived Unified Medical Language System (UMLS) concepts for representing surgical procedures. A multiple sequence alignment was computed from a collection of such sequences and was used for generating the model. These models have the potential of being useful in a variety of informatics applications such as information retrieval and automatic document generation.

Abstracting and Indexing↗

Syntactic parsing of medical reports using evolutionary optimization.

We report on a syntactic parser for medical reports which uses a genetic algorithm to efficiently identify the highest ranking parse configuration based on a scoring scheme developed as part of our prior work. The approach was tested on a set of 250 sentences from the domain of radiology. Performance time and comparison to exhaustive methods are given.

Algorithms↗

Effect of an imaging-based streamlined electronic healthcare process on quality and costs.

RATIONALE AND OBJECTIVES: A streamlined process of care supported by technology and imaging may be effective in managing the overall healthcare process and costs. This study examined the effect of an imaging-based electronic process of care on costs and rates of hospitalization, emergency room (ER) visits, specialist diagnostic referrals, and patient satisfaction. MATERIALS AND METHODS: A healthcare process was implemented for an employer group, highlighting improved patient access to primary care plus routine use of imaging and teleconsultation with diagnostic specialists. An electronic infrastructure supported patient access to physicians and communication among healthcare providers. The employer group, a self-insured company, manages a healthcare plan for its employees and their dependents: 4,072 employees were enrolled in the test group, and 7,639 in the control group. Outcome measures for expenses and frequency of hospitalizations, ER visits, traditional specialist referrals, primary care visits, and imaging utilization rates were measured using claims data over 1 year. Homogeneity tests of proportions were performed with a chi-square statistic, mean differences were tested by two-sample t-tests. Patient satisfaction with access to healthcare was gauged using results from an independent firm. RESULTS: Overall per member/per month costs post-implementation were lower in the enrolled population (126 dollars vs 160 dollars), even though occurrence of chronic/expensive diseases was higher in the enrolled group (18.8% vs 12.2%). Lower per member/per month costs were seen for inpatient (33.29 dollars vs 35.59 dollars); specialist referrals (21.36 dollars vs 26.84 dollars); and ER visits (3.68 dollars vs 5.22 dollars). Moreover, the utilization rate for hospital admissions, ER visits, and traditional specialist referrals were significantly lower in the enrolled group, although primary care and imaging utilization were higher. Comparison to similar employer groups showed that the company's costs were lower than national averages (119.24 dollars vs 146.32 dollars), indicating that the observed result was not attributable to normalization effects. Patient satisfaction with access to healthcare ranked in the top 21st percentile. CONCLUSION: A streamlined healthcare process supported by technology resulted in higher patient satisfaction and cost savings despite improved access to primary care and higher utilization of imaging.

Electronics, Medical↗

Automatic generation of repeated patient information for tailoring clinical notes.

Dictating clear, readable, and accurate clinical notes can be a time-consuming task for physicians. Clinical notes often contain information concerning the patient's medical history and current medical condition which is propagated from one clinical note to all follow-up clinical notes for the same patient. In this paper, we present a system which, given a clinical note, automatically determines what information should be repeated, and then generates this information for the physician for a new clinical note. We use semantic patterns for capturing the rhetorical category of sentences, which we show to be useful for determining whether the sentence should be repeated. Our system is shown to perform better than a baseline metric based on precision/recall results. Such a system would allow clinical notes to be more complete, timely, and accurate.

Humans↗

Inter-document coreference resolution of abnormal findings in radiology documents.

In the clinical environment, it is often necessary to track the progression of a condition or various pertinent findings over time. Establishing automatic mechanisms for tracking pertinent findings can aid in the management of a condition as well as provide feedback for treatment outcomes assessment. This work focuses on the challenge of correlating observation of pertinent findings, specifically lung masses, across documents from serial computed tomography examinations for lung cancer patients. A probabilistic model is presented to characterize the likeliness of two observed findings from different documents referring to the same entity. A greedy algorithm is also presented that utilizes the probabilistic model to establish coreference links between findings. Results from a preliminary evaluation of this methodology show a precision of 72% and a recall of 63% for the described inter-document coreference resolution task.

Algorithms↗

Automatic section segmentation of medical reports.

Automated segmentation of medical reports can significantly enhance the productivity of the healthcare departments. While many algorithms have been developed for document summarization, passage retrieval, and story segmentation of news feeds, much less effort has been devoted to parsing of medical documents. We present an algorithm specifically developed for medical applications. The algorithm consists of two components. First, a rule-based algorithm is used to detect the sections that contain labels. It utilizes a knowledge base of commonly employed heading labels and linguistic cues seen within training examples. The second part of the algorithm handles the detection of unlabeled sections. It uses a combination of lexical pattern recognition and a classifier based on an expectation model for a particular class of medical reports. The proposed method was evaluated on three test corpora containing a total of 129,303 report sections. The detection rates for labeled and unlabeled sections for individual corpus ranged from 97.4% to 99.4% and from 96.5% to 99.0%, respectively. The rule-based approach is particularly effective for medical reports due to inherently structured nature of these documents.

Algorithms↗

Workflow management of HIS/RIS textual documents with PACS image studies for neuroradiology.

Reviewing brain tumor patients' complete medical record is a daunting task for any clinician. In current practice, the radiologist examines the most recent documents and then dictates an assessment of the patient's condition based on a review of the most current imaging study and compared with the most recent previous image study. Occasionally, the radiologist searches other clinical documents when more precise detail is needed. The purpose of this research is to develop effective methods to review all of the pertinent information in a patient medical record incorporating HIS (Hospital Information Systems), RIS (Radiology Information Systems) and PACS (Picture Archiving and Communications Systems) information in three distinct ways: filtering the document worklist for pertinent clinical data, identification of key clusters of clinical information, and an automatic hanging protocol that displays the MR images for optimal image comparison.

Algorithms↗

IndexFinder: a method of extracting key concepts from clinical texts for indexing.

Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing.

Abstracting and Indexing↗

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↗

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↗

A review of medical imaging informatics.

This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content-based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general-purpose time-based multimedia data models, health-care-specific data models, knowledge models, and problem-centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer-to-peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem-specific data organization, including our problem-centric, context and user-specific visualization interface.

Databases, Factual↗

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↗

Structured reporting in neuroradiology.

We have developed a system to structure free-text neuroradiology reports using a natural language processing program and formatted the output into the digital image and communication in medicine (DICOM) standard for structured reporting (SR). DICOM SR formats the correspondence of pertinent diagnostic images to the radiologist's dictated report of clinical findings. In addition, DICOM SR allows the information to be organized into a tree structure. Individual nodes of the tree can contain individual items or lists. Structuring the content of free-text information allows the creation of hierarchies with defined relationships between the concepts contained within the report.

Brain↗

Integrated visualization of problemcentric urologic patient records.

The collision of computer-based technologies and the medical environment is resulting in an increasingly electronic multimedia patient record, consisting of not only the traditional types of data (e.g., clinic notes and laboratory reports), but also digital images (e.g., computed tomography and magnetic resonance imaging) and other visual representations of patient data (e.g., pulmonary function graphs and urodynamic charts). Given the increasing amount of data made available to physicians, it is not only critical that the totality of a patient's medical record be accessible to a clinician, but that the diverse data be integrated and presented in a manner conducive to patient management: key information should be easily discovered. This paper describes a problemcentric time-based visualization of urologic conditions, whereby a patient's medical history is automatically organized around a medical problem and presented as a graphic chronology. Urology-related data in the patient medical record is organized in accord with an expert constructed knowledge-base, and plotted on a timeline using iconic representations. The user interface permits the physician to quickly view multimedia data and to visualize relationships between events in the patient's history.

Computer Systems↗

Image study summarization of MR brain images by automated localization of relevant structures.

The paper discusses a methodology to objectify the patient presenting condition by automated selection of relevant images from a serial MR study. Structured data entry is used to capture the patient's chief complaint, pertinent history, signs, and symptoms. Expert created rules use this data to arrive at a differential and to identify the affected brain region/structure. Another expert created knowledge base then maps this information to the relevant image type, including image sequence specifics and orientation. A DICOM study reader identifies the relevant imaging sequences from the MR study. The structure localization method involves a search based on principal component analysis. A training set of subimages containing the structure of interest is used to generate a basis set of prototype images called eigenimages. The structure is located in an image by searching the image for a subregion that best matches the basis set. The structure localization was used to locate the lateral ventricles and orbits in nine images that were not part of the training set. The automated localizations were compared to expert localizations and the center of the regions located by the two techniques agreed to within +/- 1.7 mm (average for the nine localizations each of two structures).

Automation↗

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↗

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↗