An iconic language for the graphical representation of medical concepts

<p>Abstract</p> <p>Background</p> <p>Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM.</p> <p>Methods</p> <p>The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format.</p> <p>Results</p> <p>VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, <it>p </it>= 0.003) and 1.8 times faster (<it>p </it>< 0.001).</p> <p>Conclusion</p> <p>VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.</p

PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery

AbstractSchizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs

Design of a graphical and interactive interface for facilitating access to drug contraindications, cautions for use, interactions and adverse effects

<p>Abstract</p> <p>Background</p> <p>Drug iatrogeny is important but could be decreased if contraindications, cautions for use, drug interactions and adverse effects of drugs described in drug monographs were taken into account. However, the physician's time is limited during consultations, and this information is often not consulted. We describe here the design of "Mister VCM", a graphical interface based on the VCM graphical language, facilitating access to drug monographs. We also provide an assessment of the usability of this interface.</p> <p>Methods</p> <p>The "Mister VCM" interface was designed by dividing the screen into two parts: a graphical interactive one including VCM icons and synthetizing drug properties, a textual one presenting on demand drug monograph excerpts. The interface was evaluated over 11 volunteer general practitioners, trained in the use of "Mister VCM". They were asked to answer clinical questions related to fictitious randomly generated drug monographs, using a textual interface or "Mister VCM". When answering the questions, correctness of the responses and response time were recorded.</p> <p>Results</p> <p>"Mister VCM" is an interactive interface that displays VCM icons organized around an anatomical diagram of the human body with additional mental, etiological and physiological areas. Textual excerpts of the drug monograph can be displayed by clicking on the VCM icons. The interface can explicitly represent information implicit in the drug monograph, such as the absence of a given contraindication. Physicians made fewer errors with "Mister VCM" than with text (factor of 1.7; <it>p </it>= 0.034) and responded to questions 2.2 times faster (<it>p </it>< 0.001). The time gain with "Mister VCM" was greater for long monographs and questions with implicit replies.</p> <p>Conclusion</p> <p>"Mister VCM" seems to be a promising interface for accessing drug monographs. Similar interfaces could be developed for other medical domains, such as electronic patient records.</p

Introduction to Medical Coding, Introductory Module

In our increasingly global economy, manufacturers and distributers sending and receiving goods are frustrated by the reality of multiple languages. As an example of corporate coding, proactive management in an automotive company have invented a coding system where each car type has its own identification code with associated words that describe the product. Therefore, they can be confident that anyone referring to ID 4523 is describing a four-door vehicle (4000 level), compact vehicle (500), black color (20) with leather interior (3). This example is analogous to clinical or medical coding

J Biomed Inform

Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs.20152016-08-01T00:00:00ZDP2 HD084068/HD/NICHD NIH HHS/United StatesDP2HD084068/DP/NCCDPHP CDC HHS/United StatesR25 CA094186/CA/NCI NIH HHS/United StatesR25 CA094186-06/CA/NCI NIH HHS/United StatesUL1 RR024989/RR/NCRR NIH HHS/United StatesUL1 TR000439/TR/NCATS NIH HHS/United States26151312PMC4589865875

Electronic Health Record-Derived Phenotyping Models to Improve Genomic Research in Stroke

Stroke is a highly heterogeneous and complex disease that is a leading cause of death in the United States. The landscape of risk factors for stroke is vast, and its large genetic burden has yet to be fully discovered. We hypothesize that the small number of stroke variants recovered so far is due to 1) the vast phenotypic heterogeneity of stroke and 2) binary labeling of stroke genome-wide association study (GWAS) participants as cases or controls. Specifically, genome-wide association studies accumulate hundreds of thousands to millions of participants to acquire adequate signal for variant discovery. This requires time-consuming manual curation of cases and controls often involving large-scale collaborations. Genetic biobanks connected to electronic health records (EHR) can facilitate these studies by using data routinely captured during clinical care like billing diagnosis codes. These data, however, do not define adjudicated cases and controls, with many patients falling somewhere in between. There is an opportunity to use machine learning to add nuance to these definitions. We hypothesize that an expanded definition of disease by incorporating correlated diseases and risk factors from EHR data will improve GWAS power. We also hypothesize that granularly subtyping stroke using unsupervised learning methods can provide insight into stroke etiology and heterogeneity. In Chapter 1, we described the motivation for building upon current phenotyping methods for subtyping and genome-wide association studies to improve GWAS power. In Chapter 2, using patients from Columbia-New York Presbyterian (NYP) Hospital, we built and evaluated machine learning models to identify patients with acute ischemic stroke based on 75 different case-control and classifier combinations. In chapter 3, we compared two data-driven and unsupervised methods, non-negative matrix factorization (NMF) and Hierarchical Poisson Factorization, to subtype stroke patients and determined whether any of the subtypes correlate to stroke severity. In chapter 4, we estimated the heritability of acute ischemic stroke by treating the patient probabilities assigned by the machine learning phenotyping models for acute ischemic stroke in chapter 2 as a quantitative trait and mapping the probabilities to Columbia-NYP EHR-generated pedigrees. We also applied our machine learning phenotyping algorithm method, which we call QTPhenProxy, to venous thromboembolism on Columbia eMERGE Consortium patients and ran a genome-wide association study using the model probabilities as a quantitative trait. Finally, we applied QTPhenProxy to subjects in the UK Biobank for stroke and 14 other diseases and ran genome-wide association studies for each disease. We found that our machine-learned models performed well in identifying acute ischemic stroke patients in the Columbia-NYP EHR and in the UK Biobank. We also found some NMF-derived subtypes that were significantly correlated with stroke severity. We were underpowered in the eMERGE venous thromboembolism cohort GWAS and did not recover any known or new variants. Finally, we found that QTPhenProxy improved the power of GWAS of stroke and several subtypes in the UK Biobank, recovered known variants, and discovered a new variant that replicates in a previous stroke GWAS. Our results for QTPhenProxy demonstrate the promise of incorporating large but messy sets of data, such as the electronic health record, to improve signal in genome-wide association studies

Quantitative analysis of manual annotation of clinical text samples

International audienceBackground: Semantic interoperability of eHealth services within and across countries has been the main topic in several research projects. It is a key consideration for the European Commission to overcome the complexity of making different health information systems work together. This paper describes a study within the EU-funded project ASSESS CT, which focuses on assessing the potential of SNOMED CT as core reference terminology for semantic interoperability at European level.Objective: This paper presents a quantitative analysis of the results obtained in ASSESS CT to determine the fitness of SNOMED CT for semantic interoperability.Methods: The quantitative analysis consists of concept coverage, term coverage and inter-annotator agreement analysis of the annotation experiments related to six European languages (English, Swedish, French, Dutch, German and Finnish) and three scenarios: (i) ADOPT, where only SNOMED CT was used by the annotators; (ii) ALTERNATIVE, where a fixed set of terminologies from UMLS, excluding SNOMED CT, was used; and (iii) ABSTAIN, where any terminologies available in the current national infrastructure of the annotators' country were used. For each language and each scenario, we configured the different terminology settings of the annotation experiments.Results: There was a positive correlation between the number of concepts in each terminology setting and their concept and term coverage values. Inter-annotator agreement is low, irrespective of the terminology setting.Conclusions: No significant differences were found between the analyses for the three scenarios, but availability of SNOMED CT for the assessed language is associated with increased concept coverage. Terminology setting size and concept and term coverage correlate positively up to a limit where more concepts do not significantly impact the coverage values. The results did not confirm the hypothesis of an inverse correlation between concept coverage and IAA due to a lower amount of choices available. The overall low IAA results pose a challenge for interoperability and indicate the need for further research to assess whether consistent terminology implementation is possible across Europe, e.g., improving term coverage by adding localized versions of the selected terminologies, analysing causes of low inter-annotator agreement, and improving tooling and guidance for annotators. The much lower term coverage for the Swedish version of SNOMED CT compared to English together with the similarly high concept coverage obtained with English and Swedish SNOMED CT reflects its relevance as a hub to connect user interface terminologies and serving a variety of user needs

Medical Informatics

Information technology has been revolutionizing the everyday life of the common man, while medical science has been making rapid strides in understanding disease mechanisms, developing diagnostic techniques and effecting successful treatment regimen, even for those cases which would have been classified as a poor prognosis a decade earlier. The confluence of information technology and biomedicine has brought into its ambit additional dimensions of computerized databases for patient conditions, revolutionizing the way health care and patient information is recorded, processed, interpreted and utilized for improving the quality of life. This book consists of seven chapters dealing with the three primary issues of medical information acquisition from a patient's and health care professional's perspective, translational approaches from a researcher's point of view, and finally the application potential as required by the clinicians/physician. The book covers modern issues in Information Technology, Bioinformatics Methods and Clinical Applications. The chapters describe the basic process of acquisition of information in a health system, recent technological developments in biomedicine and the realistic evaluation of medical informatics