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Donald A.B. Lindberg M.D. arrived at the U.S. National Library of Medicine in 1984 and quickly launched the Unified Medical Language System (UMLS) research and development project to help computers understand biomedical meaning and to enable retrieval and integration of information from disparate electronic sources, e.g., patient records, biomedical literature, knowledge bases. This chapter focuses on how Lindberg's thinking, preferred ways of working, and decision-making guided UMLS goals and development and on what made the UMLS markedly "new and different" and ahead of its time.

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Donald A.B. Lindberg M.D. arrived at the U.S. National Library of Medicine in 1984 and quickly launched the Unified Medical Language System (UMLS) research and development project to help computer understand biomedical meaning and to enable retrieval and integration of information from disparate electronic sources, e.g., patient records, biomedical literature, knowledge bases. This chapter focuses on how Lindberg's thinking, preferred ways of working, and decision-making guided UMLS goals and development and on what made the UMLS markedly "new and different" and ahead of its time.

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OBJECTIVES: The study sought to learn if it were possible to develop an ontology that would allow the Food and Drug Administration approved indications to be expressed in a manner computable and comparable to what is expressed in an electronic health record. MATERIALS AND METHODS: A random sample of 1177 of the 3000+ extant, distinct medical products (identified by unique new drug application numbers) was selected for investigation. Close manual examination of the indication portion of the labels for these drugs led to the development of a formal model of indications. RESULTS: The model represents each narrative indication as a disjunct of conjuncts of assertions about an individual. A desirable attribute is that each assertion about an individual should be testable without reference to other contextual information about the situation. The logical primitives are chosen from 2 categories (context and conditions) and are linked to an enumeration of uses, such as prevention. We found that more than 99% of approved label indications for treatment or prevention could be so represented. DISCUSSION: While some indications are straightforward to represent, difficulties stem from the need to represent temporal or sequential references. In addition, there is a mismatch of terminologies between what is present in an electronic health record and in the label narrative. CONCLUSIONS: A workable model for formalizing drug indications is possible. Remaining challenges include designing workflow to model narrative label indications for all approved drug products and incorporation of standard vocabularies.

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This study set out to analyze questions about type 2 diabetes mellitus (T2DM) from patients and the public. The aim was to better understand people's information needs by starting with what they do not know, discovered through their own questions, rather than starting with what we know about T2DM and subsequently finding ways to communicate that information to people affected by or at risk of the disease. One hundred and sixty-four questions were collected from 120 patients attending outpatient diabetes clinics and 300 questions from 100 members of the public through the Amazon Mechanical Turk crowdsourcing platform. Twenty-three general and diabetes-specific topics and five phases of disease progression were identified; these were used to manually categorize the questions. Analyses were performed to determine which topics, if any, were significant predictors of a question's being asked by a patient or the public, and similarly for questions from a woman or a man. Further analysis identified the individual topics that were assigned significantly more often to the crowdsourced or clinic questions. These were Causes (CI: [-0.07, -0.03], p < .001), Risk Factors ([-0.08, -0.03], p < .001), Prevention ([-0.06, -0.02], p < .001), Diagnosis ([-0.05, -0.02], p < .001), and Distribution of a Disease in a Population ([-0.05,-0.01], p = .0016) for the crowdsourced questions and Treatment ([0.03, 0.01], p = .0019), Disease Complications ([0.02, 0.07], p < .001), and Psychosocial ([0.05, 0.1], p < .001) for the clinic questions. No highly significant gender-specific topics emerged in our study, but questions about Weight were more likely to come from women and Psychosocial questions from men. There were significantly more crowdsourced questions about the time Prior to any Diagnosis ([(-0.11, -0.04], p = .0013) and significantly more clinic questions about Health Maintenance and Prevention after diagnosis ([0.07. 0.17], p < .001). A descriptive analysis pointed to the value provided by the specificity of questions, their potential to disclose emotions behind questions, and the as-yet unrecognized information needs they can reveal. Large-scale collection of questions from patients across the spectrum of T2DM progression and from the public-a significant percentage of whom are likely to be as yet undiagnosed-is expected to yield further valuable insights.

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Therapeutic intent, the reason behind the choice of a therapy and the context in which a given approach should be used, is an important aspect of medical practice. There are unmet needs with respect to current electronic mapping of drug indications. For example, the active ingredient sildenafil has 2 distinct indications, which differ solely on dosage strength. In progressing toward a practice of precision medicine, there is a need to capture and structure therapeutic intent for computational reuse, thus enabling more sophisticated decision-support tools and a possible mechanism for computer-aided drug repurposing. The indications for drugs, such as those expressed in the Structured Product Labels approved by the US Food and Drug Administration, appears to be a tractable area for developing an application ontology of therapeutic intent.

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When patients cannot get answers from health professionals or retain the information given, increasingly they search online for answers, with limited success. Researchers from the United States, Ireland, and the United Kingdom explored this problem for patients with type 2 diabetes mellitus (T2DM). In 2014, patients attending an outpatient clinic (UK) were asked to submit questions about diabetes. Ten questions judged representative of different types of patient concerns were selected by the researchers and submitted to search engines within trusted and vetted websites in the United States, Ireland, and the United Kingdom. Two researchers independently assessed if answers could be found in the three top-ranked documents returned at each website. The 2014 search was repeated in June, 2016, examining the two top-ranked documents returned. One hundred and sixty-four questions were collected from 120 patients during 12 outpatient clinics. Most patients had T2DM (95%). Most questions were about diabetes (N = 155) with the remainder related to clinic operation (N = 9). Of the questions on diabetes, 152 were about T2DM. The 2014 assessment found no adequate answers to the questions in 90 documents (10 questions, 3 websites, 3 top documents). In the 2016 assessment, 1 document out of 60 (10 questions, 3 websites, 2 top documents) provided an adequate answer relating to 1 of the 10 questions. Available online sources of information do not provide answers to questions from patients with diabetes. Our results highlight the urgent need to develop novel ways of providing answers to patient questions about T2DM.

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BACKGROUND CANCER SIGNIFICANCE AND QUESTION: BioProspecting is a novel approach that enabled our team to mine genetic marker related data from the New England Journal of Medicine (NEJM) utilizing Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) and the Human Gene Ontology (HUGO). Genes associated with disorders using the Multi-threaded Clinical Vocabulary Server (MCVS) Natural Language Processing (NLP) engine, whose output was represented as an ontology-network incorporating the semantic encodings of the literature. Metabolic functions were used to identify potentially novel relationships between (genes or proteins) and (diseases or drugs). In an effort to identify genes important to transformation of normal tissue into a malignancy, we went on to identify the genes linked to multiple cancers and then mapped those genes to metabolic and signaling pathways. FINDINGS: Ten Genes were related to 30 or more cancers, 72 genes were related to 20 or more cancers and 191 genes were related to 10 or more cancers. The three pathways most often associated with the top 200 novel cancer markers were the Acute Phase Response Signaling, the Glucocorticoid Receptor Signaling and the Hepatic Fibrosis/Hepatic Stellate Cell Activation pathway. MEANING AND IMPLICATIONS OF THE ADVANCE: This association highlights the role of inflammation in the induction and perhaps transformation of mortal cells into cancers. MAJOR FINDINGS: BioProspecting can speed our identification and understanding of synergies between articles in the biomedical literature. In this case we found considerable synergy between the Oncology literature and the Sepsis literature. By mapping these associations to known metabolic, regulatory and signaling pathways we were able to identify further evidence for the inflammatory basis of cancer.

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BACKGROUND: Optimal timing and treatment of patients with concomitant head, thoracic, or abdominal injury and femoral shaft fracture remain controversial. This study examines acute patient outcomes associated with early total care with intramedullary nailing (ETC group) versus damage control external fixation (DCO group) for multiple-injured patients with femoral shaft fractures. We propose DCO as a safe initial treatment for the multiple-injured patient with femur shaft fractures. METHODS: This study was a retrospective review of the trauma registry and multisystem organ failure registry data at a Level I trauma center. Two cohorts were identified to compare multiple-injured patients with femoral shaft fractures treated with early total care and damage control orthopaedic surgery. Primary outcome measures included mortality, pulmonary complications (adult respiratory distress syndrome [ARDS] score), transfusion requirements, and multiple organ failure (MOF score). Operative time, estimated blood loss, intensive care unit length of stay (LOS), and hospital length of stay (LOS) were also compared. RESULTS: During the study period, 462 patients with 481 femoral shaft fractures were identified. Of 462 patients with femoral shaft fractures, 97 met the inclusion criteria (42 ETC and 55 DCO). The DCO group had a significantly shorter operative time (22 minutes vs. 125 minutes) and less estimated blood loss from their operative procedure (37 mL vs. 330 mL). There was no significant difference between the groups for ARDS, lung scores, MOF, MOF score, intensive care unit LOS, or hospital LOS. CONCLUSION: Fracture fixation method did not have an impact on the incidence of systemic complications in multiple-injured patients with femoral shaft fractures. Although minimal differences were noted between DCO and ETC groups regarding systemic complications, DCO is a safer initial approach, significantly decreasing the initial operative exposure and blood loss.

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BioProspecting is a novel approach that enabled our team to mine data related to genetic markers from the New England Journal of Medicine (NEJM) utilizing SNOMED CT and the Human Gene Onotology (HUGO). The Biomedical Informatics Research Collaborative was able to link genes and disorders using the Multi-threaded Clinical Vocabulary Server (MCVS) and natural language processing engine, whose output creates an ontology-network using the semantic encodings of the literature that is organized by these two terminologies. We identified relationships between (genes or proteins) and (diseases or drugs) as linked by metabolic functions and identified potentially novel functional relationships between, for example, genes and diseases (e.g. Article #1 ([Gene - IL27] = > {Enzyme - Dipeptidyl Carboxypeptidase 1}) and Article #2 ({Enzyme - Dipeptidyl Carboxypeptidase 1} < = [Disorder - Type II DM]) showing a metabolic link between IL27 and Type II DM). In this manuscript we describe our method for developing the database and its content as well as its potential to assist in the discovery of novel markers and drugs.

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To facilitate the use of standard terminologies in clinical research data collection, members of the Rare Disease Clinical Research Network have developed tools to support study investigators and research staff to code clinical research data using SNOMED CT at the point of research. This tool is customized to help the user find appropriate SNOMED CT concepts quickly, and has implications for the successful implementation of data standards to facilitate high quality research data. This paper gives an overview of an automated tool for accessing, searching, and navigating SNOMED CT real-time, at distributed and remote clinical study locations. Also, the features of the tool that enable complete data are presented, as well as possible metrics for evaluation in terms of compliance, consistency, and reliability of coding.

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NCI Thesaurus entries reference corresponding nodes in the UMLS Semantic Network (SN). Adapting a process previously used to refine relationship definitions in the UMLS Metathesaurus, we used these Thesaurus-to-Network references to analyze alignment of the Thesaurus with the OBO Relations Ontology and at the same time validate and improve Thesaurus structure. Given this experience, we offer suggestions for enhancement of the UMLS SN so that it can be even more useful in the future.

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Drug information sources use category labels to assist in navigating and organizing information. Some category labels describe drugs from multiple perspectives (e.g., both structure and function). The National Drug File - Reference Terminology (NDF RT) is a drug information source that augments a "legacy" categorization system with a formal reference model specifying Chemical Structure, Cellular or Sub-Cellular Mechanism of Action, Organ- or System-Level Physiological Effect, and Therapeutic Intent categories. We examined drug category names from three sources to better understand their information content and evaluate NDF RT's semantic coverage. On average, category names contain more than 1.5 attributes. NDF RT's reference model covers more than 76% of the information identified in drug category labels. A new NDF RT reference axis of drug formulations could improve NDF RT's coverage to 85%. The distinction between Physiological Effect and Therapeutic Intent, prompted many questions among category reviewers, suggesting that further clarification of these reference concepts is required. Careful review of existing categorization schemes may guide structured terminology and ontology development efforts toward greater fidelity to deployed information sources.

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Cancer researchers need to be able to organize and report their results in a way that others can find, build upon, and relate to the specific clinical conditions of individual patients. NCI Thesaurus is a description logic terminology based on current science that helps individuals and software applications connect and organize the results of cancer research, e.g., by disease and underlying biology. Currently containing some 34,000 concepts--covering chemicals, drugs and other therapies, diseases, genes and gene products, anatomy, organisms, animal models, techniques, biologic processes, and administrative categories--NCI Thesaurus serves applications and the Web from a terminology server. As a scalable, formal terminology, the deployed Thesaurus, and associated applications and interfaces, are a model for some of the standards required for the NHII (National Health Information Infrastructure) and the Semantic Web.

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The UMLS Metathesaurus is a syntactically uniform, concept-based, semantically enhanced representation of many of the world's authoritative biomedical vocabularies. Released several times a year, the Metathesaurus is becoming a common, longitudinally maintained source of the current versions of these vocabularies. As vocabularies become standards for reimbursement, reporting, interoperation, and use by applications, the vocabulary obtained from the Metathesaurus must be consistent with that obtainable from each vocabulary's authority. Effective with the first 2004 release, the Metathesaurus represents new and updated sources "transparently"--both users and applications are able to "see" each vocabulary in the Metathesaurus without any of the small losses of information introduced by abstractions used in previous versions. Thus, the Metathesaurus can continue to provide its many semantic and lexical value-added features while guaranteeing that original sources will be "visible" in intact form. Vocabulary users and application developers will benefit from the enhancements and economies of scale offered by the Metathesaurus, while preserving distinctions between content provided by external authorities and content added as part of the Metathesaurus development and maintenance process.

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BACKGROUND: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. METHODS: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated

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We developed and evaluated a UMLS Metathesaurus Co-occurrence mining algorithm to connect medications and diseases they may treat. Based on 16 years of co-occurrence data, we created 977 candidate drug-disease pairs for a sample of 100 ingredients (50 commonly prescribed and 50 selected at random). Our evaluation showed that more than 80% of the candidate drug-disease pairs were rated "APPROPRIATE" by physician raters. Additionally, there was a highly significant correlation between the overall frequency of citation and the likelihood that the connection was rated "APPROPRIATE." The drug-disease pairs were used to initialize term definitions in an ongoing effort to build a medication reference terminology for the Veterans Health Administration. Co-occurrence mining is a valuable technique for initializing term definitions in a large-scale reference terminology creation project.

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A semantic normal form (SNF) for a clinical drug, designed to represent the meaning of an expression typically seen in a practitioner's medication order, has been developed and is being created in the UMLS Metathesaurus. The long term goal is to establish a relationship for every concept in the Metathesaurus with semantic type "clinical drug" with one or more of these semantic normal forms. First steps have been taken using the Veterans Administration National Drug File (VANDF). 70% of the entries in the VANDF could be parsed algorithmically into the SNF. Next steps include parsing other drug vocabularies included in the UMLS Metathesaurus and performing human review of the parsed vocabularies. After machine parsed forms have been merged in the Metathesaurus Information Database (MID), editors will be able to edit matched SNFs for accuracy and establish relationships and relationship attributes with other clinical drug concepts.

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The hematopathology subcommittee of the Mouse Models of Human Cancers Consortium recognized the need for a classification of murine hematopoietic neoplasms that would allow investigators to diagnose lesions as well-defined entities according to accepted criteria. Pathologists and investigators worked cooperatively to develop proposals for the classification of lymphoid and nonlymphoid hematopoietic neoplasms. It is proposed here that nonlymphoid hematopoietic neoplasms of mice be classified in 4 broad categories: nonlymphoid leukemias, nonlymphoid hematopoietic sarcomas, myeloid dysplasias, and myeloid proliferations (nonreactive). Criteria for diagnosis and subclassification of these lesions include peripheral blood findings, cytologic features of hematopoietic tissues, histopathology, immunophenotyping, genetic features, and clinical course. Differences between murine and human lesions are reflected in the terminology and methods used for classification. This classification will be of particular value to investigators seeking to develop, use, and communicate about mouse models of human hematopoietic neoplasms.

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