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Background/Aims New terms are rapidly appearing in the literature and practice of clinical medicine and translational research. To catalog real-world usage of medical terms, we report the first construction of an online dictionary of clinical and translational medicinal terms, which are computationally generated in near real-time using a big data approach. This project is NIH CTSA-funded and developed by the Marshfield Clinic Research Foundation in conjunction with University of Wisconsin - Madison. Currently titled Marshfield Dictionary of Clinical and Translational Science (MD-CTS), this application is a Google-like word search tool. By entering a term into the search bar, MD-CTS will display that term’s definition, usage examples, contextual terms, related images, and ontological information. A prototype is available for public viewing at http://spellchecker.mfldclin.edu/.

Only 38 % of young adults with hypertension have controlled blood pressure. Lifestyle education is a critical initial step for hypertension control. Previous studies have not assessed the type and frequency of lifestyle education in young adults with incident hypertension. The purpose of this study was to determine patient, provider, and visit predictors of documented lifestyle education among young adults with incident hypertension. We conducted a retrospective analysis of manually abstracted electronic health record data. A random selection of adults 18–39 years old (n = 500), managed by a large academic practice from 2008 to 2011 and who met JNC 7 clinical criteria for incident hypertension, participated in the study. The primary outcome was the presence of any documented lifestyle education during one year after meeting criteria for incident hypertension. Abstracted topics included documented patient education for exercise, tobacco cessation, alcohol use, stress management/stress reduction, Dietary Approaches to Stop Hypertension (DASH) diet, and weight loss. Clinic visits were categorized based upon a modified established taxonomy to characterize patients’ patterns of outpatient service. We excluded patients with previous hypertension diagnoses, previous antihypertensive medications, or pregnancy. Logistic regression was used to identify predictors of documented education. Overall, 55 % (n = 275) of patients had documented lifestyle education within one year of incident hypertension. Exercise was the most frequent topic (64 %). Young adult males had significantly decreased odds of receiving documented education. Patients with a previous diagnosis of hyperlipidemia or a family history of hypertension or coronary artery disease had increased odds of documented education. Among visit types, chronic disease visits predicted documented lifestyle education, but not acute or other/preventive visits. Among young adults with incident hypertension, only 55 % had documented lifestyle education within one year. Knowledge of patient, provider, and visit predictors of education can help better target the development of interventions to improve young adult health education and hypertension control.

Objective The completion of sequencing the human genome in 2003 has spurred the production and collection of genetic data at ever increasing rates. Genetic data obtained for clinical purposes, as is true for all results of clinical tests, are expected to be included in patients’ medical records. With this explosion of information, questions of what, when, where and how to incorporate genetic data into electronic health records (EHRs) have reached a critical point. In order to answer these questions fully, this paper addresses the ethical, logistical and technological issues involved in incorporating these data into EHRs. Materials and methods This paper reviews journal articles, government documents and websites relevant to the ethics, genetics and informatics domains as they pertain to EHRs. Results and discussion The authors explore concerns and tasks facing health information technology (HIT) developers at the intersection of ethics, genetics, and technology as applied to EHR development. Conclusions By ensuring the efficient and effective incorporation of genetic data into EHRs, HIT developers will play a key role in facilitating the delivery of personalized medicine.

e14014Background: High-risk neuroblastoma (NBL) patients (pts) enrolled in a COG phase III clinical trial (ANBL0032) were randomized to isotretinoin (RA) alone or RA + immunotherapy (IM): dinutuxim...

Natural Language Processing (NLP) enables access to deep content embedded in medical texts. To date, NLP has not fulfilled its promise of enabling robust clinical encoding, clinical use, quality improvement, and research. We submit that this is in part due to poor accessibility, scalability, and flexibility of NLP systems. We describe here an approach and system which leverages cloud-based approaches such as virtual machines and Representational State Transfer (REST) to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Available architectures in which our Smntx (pronounced as semantics) system can be deployed include: virtual machines in a HIPAA-protected hospital environment, brought up to run analysis over bulk data and destroyed in a local cloud; a commercial cloud for a large complex multi-institutional trial; and within other architectures such as caGrid, i2b2, or NHIN.

Computational models in biomedicine rely on biological and clinical assumptions. The selection of these assumptions contributes substantially to modeling success or failure. Assumptions used by experts at the cutting edge of research, however, are rarely explicitly described in scientific publications. One can directly collect and assess some of these assumptions through interviews and surveys. Here we investigate diversity in expert views about a complex biological phenomenon, the process of cancer metastasis. We harvested individual viewpoints from 28 experts in clinical and molecular aspects of cancer metastasis and summarized them computationally. While experts predominantly agreed on the definition of individual steps involved in metastasis, no two expert scenarios for metastasis were identical. We computed the probability that any two experts would disagree on k or fewer metastatic stages and found that any two randomly selected experts are likely to disagree about several assumptions. Considering the probability that two or more of these experts review an article or a proposal about metastatic cascades, the probability that they will disagree with elements of a proposed model approaches 1. This diversity of conceptions has clear consequences for advance and deadlock in the field. We suggest that strong, incompatible views are common in biomedicine but largely invisible to biomedical experts themselves. We built a formal Markov model of metastasis to encapsulate expert convergence and divergence regarding the entire sequence of metastatic stages. This model revealed stages of greatest disagreement, including the points at which cancer enters and leaves the bloodstream. The model provides a formal probabilistic hypothesis against which researchers can evaluate data on the process of metastasis. This would enable subsequent improvement of the model through Bayesian probabilistic update. Practically, we propose that model assumptions and hunches be harvested systematically and made available for modelers and scientists., Author Summary Mathematical models and scientific theories fail not only from internal inconsistency, but also from the poor selection of basic assumptions. Assumptions in computational models of biomedicine are typically provided by scientists who interact directly with empirical data. If we seek to model the dynamics of cancer metastasis and ask experts regarding valid assumptions, how widely will they agree and on which assumptions? To answer this question, we queried 28 faculty-level experts about the progression of metastasis. We demonstrate an unexpected diversity of assumptions across experts leading to a striking lack of agreement over the basic stages and sequence of metastasis. We suggest a formal model and framework that builds on this diversity and enables researchers to evaluate divergent hypotheses about metastasis with experimental data. We conclude that modeling biomedical processes could be substantially improved by harvesting scientific assumptions and exposing them for formalization and experiment.

Natural Language Processing (NLP) in the medical field has the potential to dramatically influence the way in which everyday clinical care and medical research is conducted. NLP systems provide access to structured content embedded in raw medical texts, therefore enabling automated processing. There are however, several barriers prohibiting wide spread adoption of NLP technology primarily driven by the complexity and cost. This paper describes an approach and implementation which leverages cloud-based deployment and service-based interfaces to extract, process, synthesize, mine, compare/contrast, explore, and manage medical text data in a flexibly secure and scalable architecture. Through a virtual appliance architecture users are able to discover, deploy and utilize NLP engines on demand without requiring knowledge of the underlying, potentially complex, NLP engine. As highlighted in this paper, the system architecture can scale in several configurations: by increasing the number of instances deployed, the number of NLP engines, and the number of databases.

Background The third AMIA Summit on Translational Bioinformatics built on the success of the 2008 and 2009 Summits. The Summit continues to highlight the multidisciplinary nature of this rapidly maturing research field and provides the opportunity to forge new transdisciplinary collaborations as the finest minds of the academia, industry, government and non-profit sector are brought together. The six tracks spanned the range from methods for the analyses of molecular through clinical measurements and informatics methods in genetics discoveries and clinical practice. 1: Informatics Methods for the Integrative Analysis of Molecular and Clinical Measurements 2: Computational Approaches to Finding Molecular Mechanisms and Therapies for Disease 3: Informatics Concepts, Tools, and Techniques to Enable Integrative Translational Bioinformatics Research 4: Relating and Representing Phenotypes and Disease for Translational Bioinformatics Research 5: Informatics Methods Bridging Genetics Discoveries and Clinical Practice 6: Dissecting Disease through the Study of Organisms, Evolution, and Taxonomy

Understanding the molecular mechanisms underpinning prognosis and response to therapy of individuals suffering from cancer increasingly requires integrated and systematic approaches. Molecular-based strategies to more effectively prevent, diagnose, and treat cancer are seen as the future goal of oncology research. Although altered phenotypes can reliably be associated with altered gene functions, the systematic analysis of phenotypes relationships to study cancer biology remains nascent. The completion of the Human Genome Project has made possible high-throughput approaches such as the Cancer Genome Atlas to accelerate phenomics research. However, these approaches still face important challenges. In this chapter, we review these challenges, introduce current research efforts in the field, and highlight the importance of computational approaches to conduct large-scale phenomic studies.

The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset http://www.phenogo.org.

The objective of the study was to develop and implement an architecture for remote training that can be used in the narrowband home telemedicine environment. A remote training architecture, the REmote Patient Education in a Telemedicine Environment (REPETE) architecture, using a remote control protocol (RCP) was developed. A set of design criteria was specified. The developed architecture was integrated into the IDEATel home telemedicine unit (HTU) and evaluated against these design criteria using a combination of technical and expert evaluations. Technical evaluation of the architecture demonstrated that remote cursor movements and positioning displayed on the HTU were smooth and effectively real-time. The trainers were able to observe within approximately 2 seconds lag what the patient sees on their HTU screen. Evaluation of the architecture by experts was favorable. Responses to a Likert scale questionnaire regarding audio quality and remote control performance indicated that the expert evaluators thought that the audio quality and remote control performance were adequate for remote training. All evaluators strongly agreed that the system would be useful for training patients. The REPETE architecture supports basic training needs over a narrowband dial-up connection. We were able to maintain an audio chat simultaneously with performing a remote training session, while maintaining both acceptable audio quality and remote control performance. The RCP provides a mechanism to provide training without requiring a trainer to go to the patient's home and effectively supports deictic referencing to on screen objects.

This work has the objective to implement a search strategy model for high precision retrieval of documents in reply to a clinical query. To achieve this goal we designed a series of search filters on a staged configuration that allow documents to be retrieved from the most precise to the less specific. We found that for most questions the most relevant articles are retrieved at early stages of the search.

We studied the feasibility of capturing clinical questions in an inpatient setting during workflow by providing five internal medicine residents with digital recorders. We found that most of questions collected were recorded during chart documentation, that possessing the recorder prompted residents to ask more questions than they might have otherwise, and that questions were often asked only after consultation with readily available information sources such as Epocrates. Our results suggest that capturing questions during workflow on a busy hospital ward is feasible and may unmask latent information needs.

Objective To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse. Design We propose a design in which unstructured text and coded data are fused into a single model called structured narrative . Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. Validation The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. Discussion The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. Conclusion Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

Automated speech recognition (ASR) is used in many areas of medicine today. However, not many studies have evaluated the usefulness of ASR applications for capturing clinician information needs in noisy environments. We evaluated 72 ASR transcribed clinician-generated questions and assessed them for semantic and syntactic errors. The results showed that basic user training is not sufficient in order to capture the semantics of recordings.

Motivation: The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols. Results: For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast. Availability: The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006 Contact: friedman@dbmi.columbia.edu

Background Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections are becoming increasingly prevalent. There is geographic variation in their reported prevalence across the United States; however, studies reporting on CA-MRSA prevalence also demonstrate great variability in their case-finding methodology. We conducted a study to see how three different methods to ascertain CA-MRSA prevalence would lead to different estimates. Methods Different methods were used to identify cases of CA-MRSA colonization and/or infection in New York City. Method 1: retrospective review of clinical and surveillance cultures identified through a hospital computer database. Method 2: prospective collection of surveillance cultures in the same hospital's emergency department. Method 3: prospective collection of surveillance cultures in a community setting. Results Differing values for CA-MRSA prevalence resulted depending on the method and denominator used. All nares cultures as the denominator led to prevalence estimates of 0.3%-0.6%; all S. aureus as the denominator led to rates of 1.2%-5%; all MRSA as the denominator led to estimates of 5.5%-50%. Conclusions A comparison of three methods revealed that variability in case-finding methodologies can lead to different prevalence estimates. Key factors to consider when comparing CA-MRSA rates include the definition of CA-MRSA, choice of denominator, and method and setting of sample collection.

To assess the effectiveness of two serial registry reminder protocols and the interactive effects of reminders with child characteristics on immunization rates.At an inner city practice network in New York City we randomized 1662 children aged 6 weeks-15 months due or late for a diphtheria-tetanus-pertussis (DTaP) to 3 groups: continuous reminders (as needed), limited reminders (up to 3) and controls, for 6 months. Reminders were triggered by the hospital registry and immunizations were tracked with both the hospital and city registries. Analyses were based on intention to treat.At randomization, the study groups were comparable (9.2 months of age, 77% Latino, 86% Medicaid, 49.3% up-to date). A quarter of the children were sent false reminders, 15% had incorrect contact information, and 15% had missed opportunities for vaccination. In the univariate analysis, reminders improved coverage rates, but only for the children sent continuous reminders (51.2% vs. 44.9% controls, p.01). Multivariate analysis showed reminders had no independent effect on immunization outcomes. Age, up-to-date and Medicaid status at randomization were strong predictors of a child receiving any subsequent immunization. However, reminders interacted synergistically with Medicaid to increase the likelihood of receiving an immunization.At an inner city practice network, registry reminders were not effective at improving immunization outcomes due to major system barriers. Immunization registries are powerful vehicles for identifying children in need of immunizations and generating reminders but system challenges must be addressed if this promise is to be achieved in inner city practices.

BACKGROUND: Nosocomial pneumonia is the most difficult infection in terms of surveillance. The definition is complicated, and there are many opportunities for subjective divergence of opinions in determining infection status. In addition, this surveillance is extremely time-consuming for infection control practitioners (ICPs). OBJECTIVE: To compare traditional ICP surveillance for pneumonia among neonates in a level-IV neonatal intensive care unit (NICU) with computerized surveillance of chest X-ray reports using a natural language processor (NLP). METHODS: A NLP program was used to retrospectively evaluate 7928 chest X-rays from 1688 neonates in a NICU over a 2-year period (March 1, 2001–January 31, 2003). The computer program indicated which infants had pneumonia according to a set of rules derived from the National Nosocomial Infection Surveillance System (NNIS) definition as they apply to radiology reports. Data from the NLP system were compared to pneumonia data that had been collected prospectively by an ICP using the NNIS definition during the same period. RESULTS: Sensitivity of the computerized surveillance was 88%, and specificity was 94% compared with the ICP. However, the positive predictive value (PPV) was low (7.3%). CONCLUSION: The NLP methodology has the potential to extract valuable data from text reports. The PPV may be improved by refining the rules used to define pneumonia within the NLP program. If this can be accomplished, computerized surveillance may be an efficient methodology to augment surveillance in the NICU setting.

Controlled Medical Terminologies (CMTs) are indispensable tools for present medical information systems and medical informatics researches. Concept-oriented architecture and multiple-hierarchy have been accepted as two of the desiderata of contemporary CMTs. A common problem for informaticians working with class-based methods for terminologies is to find the Most Relevant Common Ancestor (MRCA) for a group of concepts, the common ancestor which has the closest semantic distance to all the given concepts. Finding an ancestor concept to summarize a group of concepts is required to map concepts from the knowledgebase to those of appropriate granularity in CMTs. However, manually exploring the hierarchical relationships and determining the MRCA are daunting tasks due to the massive size, the multiple hierarchies and in some case the cycles of the hierarchical networks of terminologies. In our study, we developed a web-based visualization tool, the Common Ancestry Summarizer (CAS), that graphically displays the hierarchical relations of multiple concepts within CMTs, and also assist users to identify the MRCA of these concepts by identifying the Lowest Common Ancestors (LCAs). The CAS has been shown useful in organizing classes for the controlled terms used in clinical guidelines (e.g. “the bottom-up method”).

Physicians' information needs routinely arise during their practice. Several studies have demonstrated that a useful way to understand the nature of the needs is to examine questions posed by physicians during the course of medical care. This paper presents an analysis of clinical questions collected when physicians were engaged in reviewing clinical cases. The main objective of the study was to investigate characteristics of the physicians' information needs to provide insight into the development of a conceptual guidance approach in information retrieval. The analysis focused on categories, search contexts, and patterns of the questions with respect to the scenarios. The results of the study showed that physicians' information needs exhibited distinct characteristics according to the scenarios, and that their needs could be expressed with a relatively small number of question patterns. These observations supported our approach of using patterns of information needs in facilitating digital information access.

We describe the use of clinical data present in the medical record to determine the relevance of research evidence from literature databases. We studied the effect of using automated knowledge approaches as compared to physician's selection of articles, when using a traditional information retrieval system. Three methods were evaluated. The first method identified terms and their semantics and relationships in the patient's record to build a map of the record, which was represented in conceptual graph notation. This approach was applied to data in an individual's medical record and used to score citations retrieved using a graph matching algorithm. The second method identified associations between terms in the medical record, assigning them semantic types and weights based on the co-occurrence of these associations in citations of biomedical literature. The method was applied to data in an individual's medical record and used to score citations. The last method combined the first two. The results showed that physicians agreed better with each other than with the automated methods. However, we found a significant positive relation between physicians' selection of abstracts and two of the methods. We believe the results encourage the use of clinical data to determine the relevance of medical literature to the care of individual patients.

Immunotherapeutic response in cancer patients, specifically those attributed to antibody-dependent cellular cytotoxicity (ADCC), may be variable dependent upon an individual's genotype. A recent Eastern Cooperative Oncology Group (ECOG) Phase III clinical trial, E4402, sought to optimize therapeutic administration of single agent rituximab (RTX) in patients with low tumor burden follicular lymphoma by investigating two RTX maintenance strategies: Arm A (RTX given "as needed") or Arm B (continuous RTX regimen). We analyzed the DNA from these patients to determine if KIR/KIR-ligand genotype had an effect on patient response to RTX. Of several parameters assessed, the activating KIR2DS1 gene and its cognate ligand, HLA-C2, were found to yield significant associations with clinical outcome. Venstrom et al. (NEJM-2012) found in an allogeneic setting that patients receiving a hematopoietic stem cell transplant from donors positive for KIR2DS1 (2DS1+) had a lower rate of relapse than from 2DS1 negative (2DS1-) donors, and this difference was further influenced by HLA-C. Specifically, for 2DS1+ donors, the HLA-C1 positive (C1+) recipients [homozygote (C1C1) or heterozygote (C1C2)] fared better than recipients homozygous for HLA-C2 (C2C2). In this study, we observed a similar finding, where 2DS1+ patients had a trend towards better response (tumor shrinkage) if they were C1+ rather than C2C2 (p=0.062), as well as a longer time to RTX-failure (TTRF) (p

Objective: A type definition, as a component of the categorical structures of a concept-oriented terminology, must support nonambiguous concept representations and, consequently, comparisons of data that are represented using different terminologies. The purpose of the study was to evaluate the adequacy and utility of a proposed type definition for nursing activity concepts. Design: Nursing activity terms ( n = 1039) from patient charts and intervention terms from two nursing terminologies (Home Health Care Classification and Omaha System) were decomposed into the attributes of the proposed type definition—Delivery Mode, Activity Focus, and Recipient. Measurements: Attributes of the type definition were coded as present or absent for each term by multiple raters. In addition, Delivery Mode was rated as Explicit or Implicit and Recipient was rated as Explicit, Implicit, or Ambiguous. The data were summarized using descriptive statistics. Inter-rater reliabilities were calculated for each attribute of the type definition. Results: All attributes of the type definition were present in 73.9 percent of the chart terms, 91.3 percent of Home Health Care Classification intervention terms, and 63.5 percent of Omaha System intervention terms. While Delivery Mode and Activity Focus were almost universally present, Recipient was problematic. It was rated as ambiguous in 4.8 percent of the chart terms, 8.7 percent of Home Health Care Classification intervention terms, and 36.5 percent of Omaha System intervention terms. Conclusions: The study findings supported the adequacy and utility of the type definition. Further research is needed to refine the type definition and its use for representing nursing activity concepts within a concept-oriented terminological system.

9583 Background: Clinical information, “-omic” datasets, and tissue samples are becoming more difficult to harmonize and manage for advanced data mining. We believe that clinical research data can be centralized and provide direct access to sample availability and associated data from a variety of information stores. Methods: We obtained a standardized set of anonymized patient data from the International Neuroblastoma Risk Group. The cohort consists of more than 11,000 children diagnosed worldwide between 1974 and 2002. The data consist of 34 metrics, such as age at diagnosis, stage of tumor, and other clinical and biological markers. We instantiated the dataset into a Postgres database, and using the Django web framework, created a data model for rapid development of tools and views and built a front-end interface for generating complex queries. To test the feasibility of accessing information on disparate and geographically distinct data samples, we have a formal agreement with the Children's Oncology Group Tumor Bank at The Research Institute at Nationwide Children's Hospital. Based on query results, we consume the Tumor Bank tissue inventory data through a web-facing application programming interface. The end-user is presented only with the number of patients who match their query search terms and for whom tissue samples are available. Results: We have completed our initial implementation and have agreements for collaboration with other international consortium groups. We have created a paradigm for statisticians to securely update and add data, and a verification system checks for internal validity and provides a report of the transaction. Our system can initiate queries and accept results in a variety of standards-compliant formats, and will be available in demonstration form by May 2012. Conclusions: Querying patient data while interrogating external sources allows researchers to observe which ancillary data and samples are available and to quickly download data or request any samples. While designed around a neuroblastoma dataset, our system can be applied to a variety of clinical scenarios and will be made available through an open-source license.

The most commonly diagnosed malignancy in the United States is non-melanoma skin cancer (NMSC) with > 2 million new cases of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) predicted for 2010. The economic burden and increasing incidence in younger populations, especially women, reinforces the value of skin cancer prevention. Health education and prior attempts at skin cancer chemoprevention have had limited success. A strong link between epithelial carcinogenesis and elevated polyamines, together with a series of phase I and II trials, led to a NCI-sponsored phase III double-blind, placebo-controlled skin cancer chemoprevention trial of α-difluoromethylornithine (DFMO) (500 mg/m2/day) for 4–5 years in 291 subjects with a history of NMSC. Subjects receiving DFMO developed significantly fewer BCC s than placebo subjects with an event rate of 0.28 BCC/person/year versus 0.40 BCC/person/year, (P = 0.03; Total BCCs: DFMO 163, placebo 243). DFMO appeared to have minimal effects on SCC incidence (DFMO 0.15 SCC/person/year, placebo 0.19, p=0.56; Total SCCs: DFMO 95, placebo 115). Subjects tolerated DFMO well, and the only significant observed toxicity was increased audiometric hearing loss. A key issue in the clinical viability of a chemoprevention agent after acute effectiveness and/or tolerance is the latent effectiveness and/or toxicity of the agent. Such effects attributable to DFMO have not been assessed. Earlier chemoprevention research has observed positive and negative latent effects with chemopreventive agents. Use of tamoxifen (5 years) for breast cancer prevention provided continued or greater protection for up to 10 years after drug stoppage. On the contrary, early work with retinoids in oral cancer prevention implied a rebound effect with subjects developing more neoplasms upon discontinuation. Also, the recent linkage between isotretinoin and inflammatory bowel disease is a concerning example of toxicity experienced years after use of a chronically administered agent. The continued interest in DFMO as a chemopreventive agent provided cause to update the clinical data and overall health status of the 291 subjects from the phase III skin cancer prevention study of DFMO to understand the sustainability of the observed DFMO effects. After institutional review board approval, we reviewed medical records of 243 subjects who received care at UW Health. Individual records were manually reviewed, and a pending comparative review of data mining by Natural Language Processing will also be completed. Both reviews focused on the skin cancer events by histology, other neoplasia (invasive and non-invasive), significant other diagnoses, and survival. The rate of skin cancer recurrence from end of study drug to the time of last medical contact (the primary endpoint) was compared between subjects randomized to DFMO or placebo using a two-sample test for primary analysis. As a secondary analysis, the rate of skin cancer recurrence from randomization to the time of last medical contact was also compared. In an exploratory analysis, the primary endpoint was analyzed using Poisson regression models to account for the baseline patient/disease characteristics at the time of randomization such as sex and age of patients and pre-study skin cancer history. Data from the 243 subjects totaling over 2,300 person years is undergoing further validation and preliminary data analysis, and will be available shortly. Citation Information: Cancer Prev Res 2011;4(10 Suppl):A52.

The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.

The large and rapidly growing number of information sources relevant to health care, and the increasing amounts of new evidence produced by researchers, are improving the access of professionals and students to valuable information. However, seeking and filtering useful, valid information can be still very difficult. An online information system that conducts searches based on individual patient data can have a beneficial influence on the particular patient's outcome and educate the healthcare worker. In this paper, we describe the underlying model for a system that aims to facilitate the search for evidence based on clinicians' needs. This paper reviews studies of information needs of clinicians, describes principles of information retrieval, and examines the role that standardized terminologies can play in the integration between a clinical system and literature resources, as well as in the information retrieval process. The paper also describes a model for a digital library system that supports the integration of clinical systems with online information sources, making use of information available in the electronic medical record to enhance searches and information retrieval. The model builds on several different, previously developed techniques to identify information themes that are relevant to specific clinical data. Using a framework of evidence-based practice, the system generates well-structured questions with the intent of enhancing information retrieval. We believe that by helping clinicians to pose well-structured clinical queries and including in them relevant information from individual patients' medical records, we can enhance information retrieval and thus can improve patient-care.