Clinical Terminology Research Articles

Brace Classification Study Group (BCSG): part one - definitions and atlas.

The current increase in types of scoliosis braces defined by a surname or a town makes scientific classification essential. Currently, it is a challenge to compare braces and specify the indications of each brace. A precise definition of the characteristics of current braces is needed. As such, the International Society for Scoliosis Orthopedic and Rehabilitation Treatment (SOSORT) mandated the Brace Classification Study Group (BCSG) to address the pertinent terminology and brace classification. As such, the following study represents the first part of the SOSORT consensus in addressing the definitions and providing a visual atlas of bracing. After a short introduction on the braces, the aim of the BCSG is described and its policies/general consideration are outlined. The BSCG endeavor embraces the very important SOSORT - Scoliosis Research Society cooperation, the history of which is also briefly narrated. This report contains contributions from a multidisciplinary panel of 17 professionals who are part of the BCSG. The BCSG introduced several pertinent domains to characterize bracing systems. The domains are defined to allow for analysis of each brace system. A first approach to brace classification based on some of these proposed domains is presented. The BCSG has reached a consensus on 139 terms related to bracing and has provided over 120 figures to serve as an atlas for educational purposes. This is the first clinical terminology tool for bracing related to scoliosis based on the current scientific evidence and formal multidisciplinary consensus. A visual atlas of various brace types is also provided.

Dione: An OWL representation of ICD-10-CM for classifying patients' diseases.

BackgroundSystematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) has been designed as standard clinical terminology for annotating Electronic Health Records (EHRs). EHRs textual information is used to classify patients’ diseases into an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) category (usually by an expert). Improving the accuracy of classification is the main purpose of using ontologies and OWL representations at the core of classification systems. In the last few years some ontologies and OWL representations for representing ICD-10-CM categories have been developed. However, they were not designed to be the basis for an automatic classification tool nor do they model ICD-10-CM inclusion terms as Web Ontology Language (OWL) axioms, which enables automatic classification. In this context we have developed Dione, an OWL representation of ICD-10-CM.ResultsDione is the first OWL representation of ICD-10-CM, which is logically consistent, whose axioms define the ICD-10-CM inclusion terms by means of a methodology based on SNOMED CT/ICD-10-CM mappings. The ICD-10-CM exclusions are handled with these mappings. Dione currently contains 391,669 classes, 391,720 entity annotation axioms and 11,795 owl:equivalentClass axioms which have been constructed using 104,646 relationships extracted from the SNOMED CT/ICD-10-CM and BioPortal mappings included in Dione using the owl:intersectionOf and the owl:someValuesFrom statements. The resulting OWL representation has been classified and its consistency tested with the ELK reasoner. We have also taken three clinical records from the Virgen de la Victoria Hospital (Málaga, Spain) which have been manually annotated using SNOMED CT. These annotations have been included as instances to be classified by the reasoner. The classified instances show that Dione could be a promising ICD-10-CM OWL representation to support the classification of patients’ diseases.ConclusionsDione is a first step towards the automatic classification of patients’ diseases by using SNOMED CT annotations embedded in Electronic Health Records (EHRs). The purpose of Dione is to standardise and formalise a medical terminology, thereby enabling new kinds of tools and new sets of functionalities to be developed. This in turn assists health specialists by providing classified information from EHRs and enables the automatic annotation of patients’ diseases with ICD-10-CM codes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0105-x) contains supplementary material, which is available to authorized users.

To adopt, to adapt, or to contextualise? The big question in clinical practice guideline development.

AimDeveloping new clinical practice guidelines (CPGs) can be time-consuming and expensive. A more efficient approach could be to adopt, adapt or contextualise recommendations from existing good quality CPGs so that the resultant guidance is tailored to the local context.ResultsThe first steps are to search for international CPGs that have a similar purpose, end-users and patients to your situation. The second step is to critically appraise the methodological quality of the CPGs to ensure that your guidance is based on credible evidence. Then the decisions begin. Can you simply ‘adopt’ this (parent) clinical practice guidelines, and implement the recommendations in their entirety, without any changes, in your setting? If so, then no further work is required. However this situation is rare. What is more likely, is that even if recommendations from the parent clinical practice guidelines can be adopted, how they are implemented needs to address local issues. Thus you may need to ‘contextualise’ the guidance, by addressing implementation issues such as local workforce, training, health systems, equipment and/or access to services. Generally this means that additional information is required (Practice/Context Points) to support effective implementation of the clinical practice guidelines recommendations. In some cases, you may need to ‘adapt’ the guidance, where you will make changes to the recommendations so that care is relevant to your local environments. This may involve additional work to search for local research, or obtain local consensus, regarding how best to adapt recommendations. For example, adaptation might reflect substituting one drug for another (drugs have similar effects, but the alternative drug to the recommended one may be cheaper, more easily obtained or more culturally acceptable). There is lack of standardisation of clinical practice guidelines terminology, leading clinical practice guideline activities often being poorly conceptualised or reported. We provide an approach that would help improve efficiency and standardisation of clinical practice guidelines activities.

Professionally responsible for counseling Pregnant Women about Abortion and Feticide

ABSTRACT We provide comprehensive, practical guidance for physicians on when to offer, recommend, perform, and refer patients for induced abortion and feticide based on the professional responsibility model of obstetric ethics. We begin by defining clinical terminology and elucidating autonomy-based and beneficencebased obligations as well as professional conscience and individual conscience. The obstetrician's role in counseling the pregnant woman about abortion and feticide should be based primarily on professional conscience, which is shaped by autonomy-based and beneficence-based obligations of the obstetrician to the pregnant and fetal patients, with important but limited constraints originating in individual conscience. How to cite this article Chervenak FA, McCullough LB. Professionally responsible for counseling Pregnant Women about Abortion and Feticide. Donald School J Ultrasound Obstet Gynecol 2016;10(3):321-325.

Teaching Clinical Reasoning to Medical Students: A Case-Based Illness Script Worksheet Approach.

Clinical reasoning is a fundamental part of a physician's daily workflow. Yet it remains a challenging skill to develop formally, especially in preclerkship-level early learners. Traditionally, medical students learn clinical reasoning informally through experiential opportunities during their clerkship years. This occurs in contrast to the more structured, explicit learning of the basic sciences and physical diagnosis during the preclerkship years. To address this need, we present a flipped classroom case-based approach for developing clinical reasoning skills based on problem representation and the use of a structured illness script worksheet as a model. Students were given a short introduction via screencast to introduce clinical reasoning and related terminology such as problem representation and semantic qualifiers. They also received a case vignette and an illness script worksheet to prepare them for in-class discussion. Students used this worksheet to practice clinical reasoning in a small-group session that was held in our last organ system-based second-year course, prior to the start of the clerkships. In comparison to the traditional facilitator-led small-group sessions, where students would sequentially answer a set of defined content-based questions to explore a clinical case, 80% of students preferred the new framework that incorporates problem representation and the illness script worksheets. Faculty facilitators found the structure of the illness script worksheet helpful in leading a clinical reasoning small-group session. Based on the results of this pilot, we plan to systematically implement this clinical reasoning framework in our preclerkship curriculum.

Atrophy, hypometabolism and clinical trajectories in patients with amyloid-negative Alzheimer’s disease

See O'Sullivan and Vann (doi:10.1093/aww166) for a scientific commentary on this article.About 15% of patients clinically diagnosed with Alzheimer's disease do not show high tracer retention on amyloid positon emission tomography imaging. The present study investigates clinical and demographic features, patterns of brain atrophy and hypometabolism and longitudinal clinical trajectories of these patients. Forty amyloid-negative patients carrying a pre-scan diagnosis of Alzheimer's disease dementia from four centres were included (11/29 females/males; mean age = 67 ± 9). Detailed clinical histories, including the clinical diagnoses before and after the amyloid scan and at follow-up, were collected. Patients were classified according to their pre-scan clinical phenotype as amnestic (memory predominant), non-amnestic (predominant language, visuospatial or frontal symptoms), or non-specific (diffuse cognitive deficits). Demographic, clinical, neuropsychological, magnetic resonance imaging and (18)F-fluorodeoxyglucose positon emission tomography data were compared to 27 amyloid-positive typical Alzheimer's disease cases (14/13 females/males; mean age = 71 ± 10) and 29 amyloid-negative controls (15/14 females/males; mean age = 69 ± 12) matched for age, gender and education. There were 21 amnestic, 12 non-amnestic, and seven non-specific amyloid-negative Alzheimer's disease cases. Amyloid-negative subgroups did not differ in age, gender or education. After the amyloid scan, clinicians altered the diagnosis in 68% of amyloid-negative patients including 48% of amnestic versus 94% of non-amnestic and non-specific cases. Amnestic amyloid-negative cases were most often reclassified as frontotemporal dementia, non-amnestic as frontotemporal dementia or corticobasal degeneration, and non-specific as dementia with Lewy bodies or unknown diagnosis. The longer-term clinical follow-up was consistent with the post-scan diagnosis in most cases (90%), including in amnestic amyloid-negative cases whose post-positon emission tomography diagnosis remained Alzheimer's disease. While the non-amnestic and non-specific amyloid-negative cases usually showed patterns of atrophy and hypometabolism suggestive of another degenerative disorder, the amnestic amyloid-negative cases had subtle atrophy and hypometabolism, restricted to the retrosplenial/posterior cingulate cortex. Patients with a negative amyloid positon emission tomography scan following an initial clinical diagnosis of Alzheimer's disease have heterogeneous clinical presentations and neuroimaging profiles; a majority showed a clinical progression that was consistent with a neurodegenerative condition. In contrast, in the subgroup of amnestic amyloid-negative cases, the clinical presentation and follow-up usually remained consistent with Alzheimer's disease. An alternative diagnosis was not made in about half of the amnestic amyloid-negative cases, highlighting the need for a clinical framework and terminology to define these patients, who may have underlying limbic-predominant, non-amyloid-related pathologies.

Biomedical Ontology Quality Assurance Using a Big Data Approach

This article presents recent progresses made in using scalable cloud computing environment, Hadoop and MapReduce, to perform ontology quality assurance (OQA), and points to areas of future opportunity. The standard sequential approach used for implementing OQA methods can take weeks if not months for exhaustive analyses for large biomedical ontological systems. With OQA methods newly implemented using massively parallel algorithms in the MapReduce framework, several orders of magnitude in speed-up can be achieved (e.g., from three months to three hours). Such dramatically reduced time makes it feasible not only to perform exhaustive structural analysis of large ontological hierarchies, but also to systematically track structural changes between versions for evolutional analysis. As an exemplar, progress is reported in using MapReduce to perform evolutional analysis and visualization on the Systemized Nomenclature of Medicine—Clinical Terms (SNOMED CT), a prominent clinical terminology system. Future opportunities in three areas are described: one is to extend the scope of MapReduce-based approach to existing OQA methods, especially for automated exhaustive structural analysis. The second is to apply our proposed MapReduce Pipeline for Lattice-based Evaluation (MaPLE) approach, demonstrated as an exemplar method for SNOMED CT, to other biomedical ontologies. The third area is to develop interfaces for reviewing results obtained by OQA methods and for visualizing ontological alignment and evolution, which can also take advantage of cloud computing technology to systematically pre-compute computationally intensive jobs in order to increase performance during user interactions with the visualization interface. Advances in these directions are expected to better support the ontological engineering lifecycle.

Clinical Reasoning: Talk the Talk or Just Walk the Walk?

O nce the purview of the research community, clinical reasoning concepts are increasingly used to inform how teachers teach and learners learn. This welcome development has been signaled by the widespread use of clinical reasoning terminology in teaching sessions, conferences, and general medical publications. Technical terminology arises in any community where foundational units of knowledge (eg, terabyte or osmosis) are necessary for members to communicate with precision and brevity. For interdisciplinary fields like clinical reasoning, terminology evolves iteratively as multiple groups think about a problem independently, develop knowledge and language simultaneously, and then use these terms to communicate between groups. Given this evolutionary process, it is helpful to periodically examine the state of terminology and its utility to the members of a community. In this issue of the Journal of Graduate Medical Education, Musgrove et al compared the frequency of clinical reasoning terms in 79 published clinical problem solving exercises to a ranking of clinical reasoning concepts by a group of educators at a single academic center. They found that educators prioritized some of the same concepts that were featured prominently in published exercises (eg, problem representation, illness script, and dual process thinking), but that published exercises placed greater emphasis on such concepts as bias and context specificity. Given the different goals of clinical instruction and case reports, this discrepancy is not surprising. Teachers often frame their instruction around undifferentiated patients in ways that put reasoning front and center in order to stimulate the triggering and sorting of common diagnoses. In contrast, published cases prioritize the retrospective review of an engaging dilemma, often resolved by an unusual diagnosis. Clinical reasoning is at the core of the reader’s journey, but whether the case illustrates a specific clinical reasoning concept is often an afterthought. The analysis by Musgrove et al raises many interesting questions about these terms and their usage, but front line educators should consider this fundamental question: Is learning clinical reasoning lingo useful for teachers or their trainees?

ADT Linked to Increased Risk of Alzheimer's Disease

The use of androgen deprivation therapy for treatment of prostate cancer was associated with increased risk of Alzheimer's disease, and patients with greater duration of ADT use had higher risks, according to medical records data analysis. ADT use was significantly associated with Alzheimer's disease risk, with a hazard ratio (HR) of 1.88 by propensity score–matched Cox regression analysis (95% CI, 1.10–3.20; P = .021), and HR of 1.66 by traditional multivariable-adjusted Cox regression analysis (95% CI, 1.05–2.64; P = .031). Patients who used ADT for 12 months or more had the greatest risk observed (HR, 2.12; 95% CI, 1.11–4.03; P = .011), and the risk increased by category of ADT duration.Investigators used a novel text-processing pipeline to analyze clinical data, extracting disease and terminology codes, medication lists, and positive-present mentions of drug and disease concepts from clinical notes. “Use of the electronic medical record in this way allows rapid investigation of a rich data source to study a broad range of postmarketing outcomes, including those unlikely to be seen in smaller clinical trials,” wrote Kevin T. Nead, MD, of the University of Pennsylvania, Philadelphia, and his colleagues [J Clin Oncol 2015 Dec 7. doi: 10.1200/JCO.2015.63.6266]. The study evaluated 16,888 patients with prostate cancer; in total, 2,397 received ADT and 125 were diagnosed with Alzheimer's disease during a median follow-up of 2.7 years. The median time to Alzheimer's disease diagnosis was 4 years. The analysis replicated previously known associations between Alzheimer's disease and age (HR, 1.06; P < .001) and cardiovascular disease (HR, 1.60; P = .031), supporting the validity of the method, according to the researchers. Jennifer Shepphird is a Frontline Medical News freelance writer based in Hermosa Beach, CA.

"Lovely Pie in the Sky Plans": A Qualitative Study of Clinicians' Perspectives on Guidelines for Managing Low Back Pain in Primary Care in England.

A qualitative study in south-west England primary care. To clarify the decision-making processes that result in the delivery of particular treatments to patients with low back pain (LBP) in primary care and to examine clinicians' perspectives on the English National Institute for Health and Care Excellence (NICE) clinical guidelines for managing LBP in primary care. Merely publishing clinical guidelines is known to be insufficient to ensure their implementation. Gaining an in-depth understanding of clinicians' perspectives on specific clinical guidelines can suggest ways to improve the relevance of guidelines for clinical practice. We conducted semi-structured interviews with 53 purposively sampled clinicians. Participants were 16 general practitioners (GPs), 10 chiropractors, 8 acupuncturists, 8 physiotherapists, 7 osteopaths, and 4 nurses, from the public sector (20), private sector (21), or both (12). We used thematic analysis. Official guidelines comprised just 1 of many inputs to clinical decision-making. Clinicians drew on personal experience and inter-professional networks and were constrained by organizational factors when deciding which treatment to prescribe, refer for, or deliver to an individual patient with LBP. Some found the guideline terminology-"non-specific LBP"-unfamiliar and of limited relevance to practice. They were frustrated by disparities between recommendations in the guidelines and the real-world situation of short consultation times, difficult-to-access specialist services, and sparse commissioning of guideline-recommended treatments. The NICE guidelines for managing LBP in primary care are one, relatively peripheral, influence on clinical decision-making among GPs, chiropractors, acupuncturists, physiotherapists, osteopaths, and nurses. When revised, these guidelines could be made more clinically relevant by: ensuring that guideline terminology reflects clinical practice terminology; dispelling the image of guidelines as rigid and prohibiting patient-centered care; providing opportunities for clinicians to engage in experiential learning about guideline-recommended complementary therapies; and commissioning guideline-recommended treatments for public sector patients. N/A.

의학 용어의 효과적인 검색을 위한 SNOMED CT 브라우저의 구현

SNOMED CT 표준 의학 용어 체계를 활용하여 진료기록을 작성하기 위해서는 용어 체계에 포함된 방대한 양의 용어들 중 적합한 용어를 빠른 시간에 선택하는 것이 필요하다. 기존의 검색 브라우저들은 검색 결과를 단순 목록으로 나열하므로 유사한 용어들을 사용자가 구분하여 선택하기 어려운 문제가 있다. 본 논문에서는 용어체계의 특성을 고려하여 효과적인 용어의 검색이 가능한 브라우저를 제안한다. 제안하는 시스템에서는 간략화된 트리 형태로 검색 결과의 계층적 구조를 함께 표시하므로 검색 결과 용어들 중 원하는 용어를 빠르게 선택할 수 있다. 그리고 시스템 설계와 구현을 통해 제안하는 방법의 효과성을 입증한다. To write a medical record using SNOMED CT standard clinical terminologies, it is necessary to find and select an appropriate terminology from the huge volume of terminologies within short time. Using previous SNOMED CT search browsers, it is very difficult to select appropriate one from search results since they provide a simple list-up of similar candidate terminologies. This paper proposes a novel search browser which supports effect searching of clinical terminology by utilizing characteristics of SNOMED CT. The proposed system provides a simplified tree-view representing hierarchical structures of search results which enables fast selection of appropriate terminology from the search results. Design and Implementation of the system proves effectiveness of the proposed approach.

Clues to the Blues: Predictors of Self-Reported Mental and Emotional Health Among Older African American Men.

The mental health needs of aging African American men have been overlooked and few studies have distinguished between more severe clinically diagnosable mental health challenges and less severe emotional states for this population. African American men may not identify with or internalize the terminology of "depression" despite exhibiting the symptom criteria. This exploratory cross-sectional study examined correlates of "downheartedness" as an alternative indicator of emotional health. The authors examined the self-reported responses of 1,666 older African American men on a baseline questionnaire from a larger longitudinal study. Demographic, physical, mental and emotional health, and health system factors were examined as possible correlates of downheartedness. The mean age of participants was 73.6 years and 74.8% of men described themselves as "downhearted and blue" most or all of the time while only 18.5% of them reported feeling moderate to severe anxiety or depression. When other factors were controlled, mobility problems (odds ratio [ OR] = 2.36), problems getting health care ( OR = 2.69), having a doctor who never listens ( OR = 2.18), physical or mental problems that interfere with social activities ( OR = 1.34), accomplishing less due to physical health ( OR = 1.35), and accomplishing less due to mental/emotional health ( OR = 1.57) were all associated with greater odds of being downhearted. The current findings indicate that this sample more closely identified with language accurately describing their emotional health state (i.e., downhearted) and not with clinical mental health terminology (i.e., depression) that may be culturally stigmatized.

Developing educational iPhone, Android and Windows smartphone cross-platform apps to facilitate understanding of clinical genomics terminology

Clinicians are increasingly using genomic technologies to determine the molecular basis of presenting conditions, identify increased risk factors or guide treatment (Deciphering Developmental Disorders Study, 2015). Unfortunately, however, the bioinformatic terms and acronyms that are frequently used in the new field of Clinical Genomics (the study of all genes simultaneously, rather than of individual genes as in Clinical Genetics) can often be unfamiliar to many health professionals (Institute of Medicine Roundtable on Translating Genomic-Based Research for Health, 2015). These include clinicians who, increasingly, need to be able to accurately interpret clinical genomics-related laboratory reports and to understand genomics research papers and seminars. This is in addition to the requirement for these individuals to participate in discussions with genetics lab scientists and, on occasion, bioinformaticians. Unfortunately, online information can be unhelpful as the genomics definitions provided on the internet are often written for readers with much previous genomics-related knowledge. Excellent online summaries of genetic conditions and of standard genetics terms are available via GeneReviews (http://www.ncbi.nlm.nih.gov/books/NBK1116/), UpToDate (http://www.uptodate.com/home) and the National Human Genome Research Institute (https://www.genome.gov). These sources, however, currently do not provide explanations of many of the technical terms that relate to the field of genomics. Moreover, mobile internet access can be intermittent or slow, for instance, on public transport in the UK. A self-contained mobile app (application), in contrast, can provide a user-friendly, rapid and accessible alternative information source with its customized contained information being available offline. Furthermore, a high proportion of professionals and postgraduate students now use Apple (iOS), Android or Windows smartphones (Sandholzer et al., 2015, Payne et al., 2012). 1.1. Objectives The objective was primarily to provide a concise bioinformatics dictionary for clinicians that is easy to access and to understand. Specifically, the aim was to create a smartphone app containing a small glossary of commonly used (but often unfamiliar) genomics terms (eg “FASTQ” and “PED file”) that would be user-friendly, easily accessible, self-contained and accompanied by illustrated explanations. The app containing this information would, ideally, be capable of running on Android or iOS portable devices and on a PC. An additional objective was to create an interactive quiz as a self-assessment system to test the learning of this information, with a personal points score. Currently, no similar electronic mobile self-contained glossary exists that covers the highly specialized terms used in clinical genomics.

An alternative database approach for management of SNOMED CT and improved patient data queries

ObjectiveSNOMED CT is the international lingua franca of terminologies for human health. Based in Description Logics (DL), the terminology enables data queries that incorporate inferences between data elements, as well as, those relationships that are explicitly stated. However, the ontologic and polyhierarchical nature of the SNOMED CT concept model make it difficult to implement in its entirety within electronic health record systems that largely employ object oriented or relational database architectures. The result is a reduction of data richness, limitations of query capability and increased systems overhead. The hypothesis of this research was that a graph database (graph DB) architecture using SNOMED CT as the basis for the data model and subsequently modeling patient data upon the semantic core of SNOMED CT could exploit the full value of the terminology to enrich and support advanced data querying capability of patient data sets. MethodsThe hypothesis was tested by instantiating a graph DB with the fully classified SNOMED CT concept model. The graph DB instance was tested for integrity by calculating the transitive closure table for the SNOMED CT hierarchy and comparing the results with transitive closure tables created using current, validated methods. The graph DB was then populated with 461,171 anonymized patient record fragments and over 2.1 million associated SNOMED CT clinical findings. Queries, including concept negation and disjunction, were then run against the graph database and an enterprise Oracle relational database (RDBMS) of the same patient data sets. The graph DB was then populated with laboratory data encoded using LOINC, as well as, medication data encoded with RxNorm and complex queries performed using LOINC, RxNorm and SNOMED CT to identify uniquely described patient populations. ResultsA graph database instance was successfully created for two international releases of SNOMED CT and two US SNOMED CT editions. Transitive closure tables and descriptive statistics generated using the graph database were identical to those using validated methods. Patient queries produced identical patient count results to the Oracle RDBMS with comparable times. Database queries involving defining attributes of SNOMED CT concepts were possible with the graph DB. The same queries could not be directly performed with the Oracle RDBMS representation of the patient data and required the creation and use of external terminology services. Further, queries of undefined depth were successful in identifying unknown relationships between patient cohorts. ConclusionThe results of this study supported the hypothesis that a patient database built upon and around the semantic model of SNOMED CT was possible. The model supported queries that leveraged all aspects of the SNOMED CT logical model to produce clinically relevant query results. Logical disjunction and negation queries were possible using the data model, as well as, queries that extended beyond the structural IS_A hierarchy of SNOMED CT to include queries that employed defining attribute–values of SNOMED CT concepts as search parameters. As medical terminologies, such as SNOMED CT, continue to expand, they will become more complex and model consistency will be more difficult to assure. Simultaneously, consumers of data will increasingly demand improvements to query functionality to accommodate additional granularity of clinical concepts without sacrificing speed. This new line of research provides an alternative approach to instantiating and querying patient data represented using advanced computable clinical terminologies.

Genome interpretation: Clinical correlation is recommended

Genome interpretation: Clinical correlation is recommended

Looking forward to looking back

Looking forward to looking back

Louhi 2014: Special issue on health text mining and information analysis.

Mining and automating analysis of information from health documents holds great potential for improving health care in many aspects. Health documents include text sources such as medical records, scientific publications, and user-generated texts in e.g. social media. Research in the area of health text mining has grown and matured in recent years. Research on texts written in English is dominating, but in the last ten years research on other languages has evolved, including work on French, Spanish, Swedish, Danish, Finnish, German, Chinese and Japanese health documents. Initially, scientific biomedical articles were the main data source, after which research on clinical notes, blogs and patient forums, etc. has also increased. Moreover, the number of practical applications has increased [1], including tools for de-identification of Protected Health Information (PHI) in clinical text [2], automatic diagnosis code assignment [3], clinical decision support [4], detection of adverse drug events [5], as well as other adverse hospital events such as detection of hospital acquired infections [6], extraction of structured information from pathology reports [7], text simplification of clinical text for patient use [8], as well as pre-processing pipelines and information extraction in tools such as cTAKES [9]. Working with sensitive data such as medical records requires considerable efforts and considerations in terms of ensuring individual privacy, which makes access to such data an obstacle. Through the organization of shared tasks such as i2b2 (https://www.i2b2.org/NLP/, accessed 2 Nov. 2014), ShARe/CLEF eHealth (http://clefehealth2014.dcu.ie/, accessed 2 Nov. 2014) and SemEval (http://alt.qcri.org/semeval2014/task7/, accessed 2 Nov. 2014), English datasets have been made more easily available for research groups, which has been a crucial step towards strengthening method development and ensuring comparability as well as reproducibility. For other languages, very few such resources are currently available. Similarly, a very important resource for performing research in the health text domain is the availability of terminologies and ontologies. SNOMED CT is a global comprehensive clinical terminology available in English, French, Danish, Dutch, Spanish and Swedish. Extending this and similar terminologies to new languages is a necessity. This supplement aims at contributing to the research community in the area of information analysis in the health domain by presenting work describing approaches, solutions and progress in the challenges identified above. Specifically, it is of importance that research on languages other than English is presented, as well as novel data resources. Research in natural language processing and text mining of health documents is increasing and has extended to several new languages such as German, Finnish, Spanish and Basque, as well as to new application areas. We believe that the evolvement and progress in this area will increase the efficiency and quality of health care in the near future.

A Comparative study on Chinese Western Medicine diagnoses and related outcomes for different types of stroke

Purpose: Currently in Hong Kong, health-related and medical data are usually created and kept by different healthcare providers (or sometimes by individual patients) at different locations in different formats, e.g. at CMP’s clinics and herbalists shops.While somehealthcareprovidersmaydeploy electronicmedical/patient record systems to store and retrieve medic al/patient data, such systems are generally not capable of data sharing at any large scale if at all. An eHR Sharing System provides an information infrastructure for healthcare providers in both the public and private healthcare sectors, with informed and express consent of the patient and proper authorisation for access to the System, to share the eHR they keep on the patient with other healthcare providers and to retrieve the eHR of the patient shared by other healthcare providers. Methods: During the first stage of the project, standardisation of clinical terms mainly focused on four domains including diseases ( ), patterns ( ), acupoints ( ) and interventions ( ). Results: The initial design and architecture of the Chinese Medicine Clinical Terminology Table (CMCTT) has been established. Common terms in relation to diseases, acupoints, patterns and inventions were analysed and incorporated into the CMCTT. Conclusion: Standardisation of CM information forms the basis for accurate and efficient communication of electronic CM data. It facilitates uniform communications and reduces costs of technical integration. A proper management framework on standard development lifecycle will ensure the concepts are properly created, described and organised which will enhance data accuracy and quality for health information exchange. Both CM Terminology tables and the maintenance process are essential to the development and daily operation of terminology standard to support data sharing to the eHR. Contact: Ruth LEUNG, lhw417@ha.org.hk

Seventeen Years of Using Flowable Resin Restoratives--A Dental Practitioner's Personal Clinical Review.

Seen through the author's eyes on the basis of his practising dentistry for almost three decades, light-activated flowable resin restoratives (FRCs) or, in common clinical dental terminology, flowable composites have gradually gained major importance in restorative dentistry. Inputs to this ongoing trend are coming from continuous improvements in material properties and the favourable handling characteristics experienced with this particular group of restoratives. Intended to be used in direct adhesive filling procedures, the number and variety of recent generations of flowable composites for lining, restoration of all cavity classes (I-V), core build-ups and, more recently, 'bulk-fill-restorations', however, necessitates a profound clinical understanding of the selective use of flowable composites to ensure clinical success and guarantee long-term high quality results. Clinical relevance: Today's flowable composites allow for reliable restoration of all kinds of defects. However, both the handling characteristics and the material properties of FRCs must be fully understood before taking advantage of their potentially excellent clinical performance.

Building bridges across electronic health record systems through inferred phenotypic topics

ObjectiveData in electronic health records (EHRs) is being increasingly leveraged for secondary uses, ranging from biomedical association studies to comparative effectiveness. To perform studies at scale and transfer knowledge from one institution to another in a meaningful way, we need to harmonize the phenotypes in such systems. Traditionally, this has been accomplished through expert specification of phenotypes via standardized terminologies, such as billing codes. However, this approach may be biased by the experience and expectations of the experts, as well as the vocabulary used to describe such patients. The goal of this work is to develop a data-driven strategy to (1) infer phenotypic topics within patient populations and (2) assess the degree to which such topics facilitate a mapping across populations in disparate healthcare systems. MethodsWe adapt a generative topic modeling strategy, based on latent Dirichlet allocation, to infer phenotypic topics. We utilize a variance analysis to assess the projection of a patient population from one healthcare system onto the topics learned from another system. The consistency of learned phenotypic topics was evaluated using (1) the similarity of topics, (2) the stability of a patient population across topics, and (3) the transferability of a topic across sites. We evaluated our approaches using four months of inpatient data from two geographically distinct healthcare systems: (1) Northwestern Memorial Hospital (NMH) and (2) Vanderbilt University Medical Center (VUMC). ResultsThe method learned 25 phenotypic topics from each healthcare system. The average cosine similarity between matched topics across the two sites was 0.39, a remarkably high value given the very high dimensionality of the feature space. The average stability of VUMC and NMH patients across the topics of two sites was 0.988 and 0.812, respectively, as measured by the Pearson correlation coefficient. Also the VUMC and NMH topics have smaller variance of characterizing patient population of two sites than standard clinical terminologies (e.g., ICD9), suggesting they may be more reliably transferred across hospital systems. ConclusionsPhenotypic topics learned from EHR data can be more stable and transferable than billing codes for characterizing the general status of a patient population. This suggests that EHR-based research may be able to leverage such phenotypic topics as variables when pooling patient populations in predictive models.