Empirical derivation of an electronic clinically useful problem statement system.

Analyzing polysemous concepts from a clinical perspective: Application to auditing concept categorization in the UMLS

Significance. Early detection of axial spondyloarthritis (axSpA) is a complex clinical task. Quality improvement of axSpA diagnostics in primary care settings is possible with the help of decision-making information systems based on the ontological approach application. The key stage of the decision-making system development consists of the elaboration of a set of clinical terms. This set should fully describe clinical area or sub-area under study. One of the essential requirements is compliance of the clinical terms used with the existing clinical nomenclatures. Currently, the largest set of clinical terms is the Unified Medical Language System (UMLS) metathesaurus. The majority of UMLS terms is presented in English only. The development of tools for the analysis of unstructured texts and recognition of clinically relevant UMLS entities make it possible to elaborate a set of terms describing axSpA diagnostic aspects. This will also help to compile a list of UMLS terminology nomenclatures for their priority adaptation and expert translation into Russian. The purpose of this study is to develop an automated system for recognizing clinically relevant UMLS terms in texts of the English-language articles. Material and methods. The research material has included English terms (11.2 million) aggregated from 76 nome6nclatures of the current UMLS (2022AB) version. In addition, the study has used texts of PubMed clinical abstracts in English. Queries to the UMLS graph model, semantic algorithms for unstructured texts and machine-learning methods have been applied for data collection and analysis. Results. The study has elaborated a set of high-accuracy regular expressions (F1-score=98%) for metadata elimination from the text corpus. Then the authors have identified patterns for searching clinically relevant terms in the aggregated set of UMLS concepts. Using a logistic regression algorithm, the authors have trained a binary classification model. Input data for the created classificatory are information about an UMLS term. Output data are a label indicating the presence or absence of clinical relevance. Conclusion. The binary classification model has been validated individually and double-tested on different data samples. Values of the accuracy, sensitivity and specificity of metrics equal to 91%, 90% and 91%, respectively, for the validation sample (a number of axSpA terms). In addition, this model has been tested on the sets of terms aggregated for any two diseases. Values of the accuracy metric equal to 91% and 90%, respectively. With the help of the developed machine-learning model, the study has estimated that UMLS contains 1.5 million unique terms applicable to describing a clinical picture. In addition, lists of priority UMLS data sources and thematic groups have been compiled. These clinically relevant UMLS terms should be adapted and translated into Russian as soon as possible.

This paper describes preliminary work evaluating automated semantic indexing of radiology imaging reports to represent images stored in the Image Engine multimedia medical record system at the University of Pittsburgh Medical Center. The authors used the SAPHIRE indexing system to automatically identify important biomedical concepts within radiology reports and represent these concepts with terms from the 1998 edition of the U.S. National Library of Medicine's Unified Medical Language System (UMLS) Metathesaurus. This automated UMLS indexing was then compared with manual UMLS indexing of the same reports. Human indexing identified appropriate UMLS Metathesaurus descriptors for 81% of the important biomedical concepts contained in the report set. SAPHIRE automatically identified UMLS Metathesaurus descriptors for 64% of the important biomedical concepts contained in the report set. The overall conclusions of this pilot study were that the UMLS metathesaurus provided adequate coverage of the majority of the important concepts contained within the radiology report test set and that SAPHIRE could automatically identify and translate almost two thirds of these concepts into appropriate UMLS descriptors. Further work is required to improve both the recall and precision of this automated concept extraction process.

Efficient clinical information retrieval tasks like entity recognition, relation suggestions, summarization etc, require a comprehensive, extensive, unambiguous and well structured medical knowledge base. One of the largest Metathesaurus, UMLS (Unified Medical Language System), is a repository of biomedical dictionaries developed by the US National Library of Medicine (NLM) and widely used in medical domain. UMLS Metathesaurus includes the dictionaries like SNOMED-CT, National Center for Biotechnology Information (NCBI) taxonomy, Medical Subject Headings (MeSH), Online Mendelian Inheritance in Man (OMIM) etc. As it has integrated data from different sources, it contains different kinds of ambiguity, which is problematic for all clinical information retrieval tasks that use it. In this paper, we describe our methodology of curating the UMLS metathesaurus to create a centralized knowledge base that can be used as a knowledge base for a variety of clinical NLP systems. We have also developed a process of updating the curated centralized knowledge base with a newer version of UMLS such that there is no need to repeat the whole process. We have also presented the comparative results of a Clinical Entity Recognition (CER) using our curated centralized knowledge base and original UMLS database.

This paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user's query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts' terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical terms, especially when these terms are limited by our DSP algorithm.

We have developed a semantic network of biological terminology to aid in the retrieval and integration of biological information from a variety of disparate information sources. Our semantic network strives to provide a categorization of biological concepts and relationships among these concepts. The semantic network will impart a knowledge structure through which computers can "reason " and draw conclusions about biological data objects and will provide a federated view of the many disparate databases of interest to biologists. In the development of our system, we have included the concepts from several established controlled vocabularies, chief among them being the National Library of Medicine's Unified Medical language System (UMLS). While the UMLS Metathesaurus provides an excellent controlled vocabulary, we have found their semantic network lacking in sufficient detail to be useful as a tool for categorization of biological concepts in databases. We would like to provide a categorization of concepts that provides finer detail than their semantic network without the considerable size and complexity of their Metathesaurus. Our complete semantic network consists of 183 semantic types and 69 relationships.

BackgroundWhile Cryptococcus neoformans is a major cause of morbidity and mortality among HIV-infected persons worldwide, there is scarce recent data on disease prevalence in the United States, including in Southeastern states, where HIV rates are high. We sought to determine the prevalence of cryptococcal disease and compare the performance of a cryptococcal antigen (CrAg) lateral flow assay (LFA) vs. latex agglutination (LA) test.MethodsAll patients from Grady Memorial Hospital in Atlanta, Georgia who had a serum or cerebrospinal fluid (CSF) sample sent for CrAg LA testing as part of routine management from November 2017 to July 2018 were included. The LFA was performed on all samples by research staff; results were not available to clinicians. Rates of disease and agreement between the LA test and LFA were calculated.ResultsAmong 467 patients, 570 LA tests were performed; 417 on serum and 153 on CSF (87 patients with multiple tests performed). Mean age was 44 years, and most were male (n = 322, 69%). Most patients had HIV (n = 371, 79%); median CD4 count was 73 cells/mm3 and 77% were not receiving ART. Among HIV-infected individuals, testing was performed equally in the inpatient and outpatient setting. Cryptococcal testing was done in 53 persons without apparent risk factors. Thirty-three (7%) patients had a positive serum or CSF test. Five (1%) patients had both a positive serum and CSF LA test and LFA. While the overall agreement between the LA test and LFA was substantial to high for CSF (κ = 0.71) and serum (κ = 0.93), respectively, there were important discrepancies. Four patients with a negative serum LA test had a positive serum LFA. Five patients had false-positive CSF LA tests, determined by negative CSF LFA testing, India ink, and CSF and fungal cultures. All were treated with amphotericin and flucytosine with one patient experiencing a severe anaphylactic reaction to amphotericin.ConclusionWe found a moderately high rate of cryptococcal disease and important discrepancies between the LA test and LFA. The LFA appeared to be more sensitive for cryptococcemia and more specific for meningitis. Clinical implications of these findings include earlier detection and treatment of cryptococcemia, and averting unnecessary treatment of meningitis with costly medications associated with high rates of adverse events.DisclosuresAll authors: No reported disclosures.

The Unified Medical Language System metathesaurus and medical semantic network (National Library of Medicine, Meta 1.3, 1993) currently contains 270,797 medical terms derived from multiple controlled vocabularies, including ICD-9, ACR, MeSH, CPT, and SNOMED. We briefly describe the structure and content of Meta 1.3, focusing on its potential value to the radiological community.

e11582 Background: Breast cancer mortality has historically been higher in African American (AA) women compared to Caucasian women. Controlling for tumor characteristics, biological markers and comorbidities does not account for the disparity. Previous studies have shown that AA women are more likely to have a high 21-gene recurrence score compared to Caucasian women. Another potential variable affecting treatment outcomes is adherence to adjuvant endocrine therapy (ET). We conducted a retrospective review of pharmacy records at Grady Memorial Hospital (GMH) to examine the non-adherence rate among predominantly AA patients (pts). Methods: Pharmacy database records were examined for pts filling prescriptions for tamoxifen, anastrozole, or letrozole at GMH in Atlanta, Georgia from 2004-2009. Baseline characteristics were obtained by chart review. Pts were excluded if they had metastatic disease, DCIS, less than 60 days of eligible prescription, were male, deceased during the study period, benign disease, or no documentation of breast cancer in the electronic medical record (EMR). Non-adherence was defined as those filling less than 80% of eligible days covered by her prescription. Results: 679 pts were identified who filled prescriptions for tamoxifen, anastrozole, or letrozole at Grady pharmacy from 2004-2009. Pts who were excluded had metastatic disease (152), DCIS (101), had less than 60 days of eligible prescription (65), had no documentation of breast cancer in the EMR (25), were male (6), deceased before 2009 (5), or had benign disease (9). Of the 316 pts eligible for the study, median age was 60 (26 to 94) at time of diagnosis, 86% were AA, and 39% were node positive. 167 pts filled prescriptions for tamoxifen, 95 for anastrozole, and 54 for letrozole. The non-adherence rate for tamoxifen was 50%, anastrozole 38%, and letrozole 48%. Overall non-adherence rate was 46%. Conclusions: The overall non-adherence rate to adjuvant ET among a predominately AA population seen in a county hospital was similar to previously reported rates. Non-adherence to ET in this underserved AA pt population does not fully account for disparate outcomes.

Data mining is a process of discovering useful information from a database and analysis of extracted information. Text mining uses many techniques of data mining. It primarily deals with unstructured data. Web mining is an extension of text mining since it deals with unstructured data. Data mining relates to find data from “static databases” which contains “structured” data where as, web mining plays with data that are “dynamic” and “unstructured”. In this papers our goal is to mine biomedical data from hypertext documents (e.g., mining data from web contents) using text mining techniques with the help of “biomedical ontology”. Web data repositories are the hypertext documents. Texts in the Hypertext documents are unstructured and they contain Hypertext Markup Language (HTML) tags, scripting languages, images, audios, videos, URLs etc. We collect a number of documents using Google crawler and preprocess the hypertext documents and extract the text data. Next, we identify whether a word is a biomedical entity or not by using a biomedical database the “Unified Medical Language System (UMLS) metathesaurus”. The mapping of biomedical entity from the metathesaurus will be done based on keyword query. Then we apply the result to re-rank the web documents to find most relevant documents. We conclude that the more occurrence of a biomedical entity in a page, the more relevant the page is, and thus, we can re-rank the documents to find the most relevant documents by using text mining technique.

BackgroundRadiology reporting is a clinically oriented form of documentation that reflects critical information for patients about their health care processes. Realizing its importance, many medical institutions have started providing radiology reports in patient portals. The gain, however, can be limited because of medical language barriers, which require a way for customizing these reports for patients. The open-access, collaborative consumer health vocabulary (CHV) is a terminology system created for such purposes and can be the basis of lexical simplification processes for clinical notes.ObjectiveThe aim of this study was to examine the comprehensibility and suitability of CHV in simplifying radiology reports for consumers. This was done by characterizing the content coverage and the lexical similarity between the terms in the reports and the CHV-preferred terms.MethodsThe overall procedure was divided into the following two main stages: (1) translation and (2) evaluation. The translation process involved using MetaMap to link terms in the reports to CHV concepts. This is followed by replacing the terms with CHV-preferred terms using the concept names and sources table (MRCONSO) in the Unified Medical Language System (UMLS) Metathesaurus. In the second stage, medical terms in the reports and general terms that are used to describe medical phenomena were selected and evaluated by comparing the words in the original reports with the translated ones. The evaluation includes measuring the content coverage, investigating lexical similarity, and finding trends in missing concepts.ResultsOf the 792 terms selected from the radiology reports, 695 of them could be mapped directly to CHV concepts, indicating a content coverage of 88.5%. A total of 51 of the concepts (53%, 51/97) that could not be mapped are names of human anatomical structures and regions, followed by 28 anatomical descriptions and pathological variations (29%, 28/97). In addition, 12 radiology techniques and projections represented 12% of the unmapped concepts, whereas the remaining six concepts (6%, 12/97) were physiological descriptions. The rate of lexical similarity between the CHV-preferred terms and the terms in the radiology reports was approximately 72.6%.ConclusionsThe CHV covered a high percentage of concepts found in the radiology reports, but unmapped concepts are associated with areas that are commonly found in radiology reporting. CHV terms also showed a high percentage of lexical similarity with terms in the reports, which contain a myriad of medical jargon. This suggests that many CHV terms might not be suitable for lay consumers who would not be facile with radiology-specific vocabulary. Therefore, further patient-centered content changes are needed of the CHV to increase its usefulness and facilitate its integration into consumer-oriented applications.

In this paper, we present a knowledge-assisted approach to index and retrieve large volume of medical images. Both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework for modular acquisition of medical semantics from images with complementary global and local image indexing schemes. Two fusion approaches are also developed to improve text retrieval using the UMLS-based image indexing: a simple post-query fusion and a visual modality filtering to remove visually aberrant images according to the query modality concepts. On the ImageCLEFmed 2005 database, our framework outperformed our previous result which ranked top in the ImageCLEFmed 2005 Medical Image Retrieval task benchmark.

The Unified Medical Language System (UMLS) Metathesaurus enables the standardized representation of healthcare information so that it can be correctly stored, manipulated, retrieved, and exchanged.

Imported Malaria at an Inner-City Hospital in the United States

This paper describes our participation in task 7 of SemEval 2014, which focuses on analysis of clinical text. The task is divided into two parts: recognizing mentions of concepts that belong to the UMLS (Unified Medical Language System) semantic group disorders, and mapping each disorder to a unique UMLS CUI (Concept Unique Identifier), if possible. For identifying and mapping disorders belonging to the UMLS meta thesaurus, we explore two tools: Metamap and T-mapper. Additionally, a Named Entity Recognition system, based on a maximum entropy model, was implemented to identify other disorders.