Next-generation sequencing in neuromuscular diseases.

Curr Opin Neurol. 2016 Oct;29(5):527-536

Authors: Efthymiou S, Manole A, Houlden H

Abstract
PURPOSE OF REVIEW: Neuromuscular diseases are clinically and genetically heterogeneous and probably contain the greatest proportion of causative Mendelian defects than any other group of conditions. These disorders affect muscle and/or nerves with neonatal, childhood or adulthood onset, with significant disability and early mortality. Along with heterogeneity, unidentified and often very large genes require complementary and comprehensive methods in routine molecular diagnosis. Inevitably, this leads to increased diagnostic delays and challenges in the interpretation of genetic variants.
RECENT FINDINGS: The application of next-generation sequencing, as a research and diagnostic strategy, has made significant progress into solving many of these problems. The analysis of these data is by no means simple, and the clinical input is essential to interpret results.
SUMMARY: In this review, we describe using examples the recent advances in the genetic diagnosis of neuromuscular disorders, in research and clinical practice and the latest developments that are underway in next-generation sequencing. We also discuss the latest collaborative initiatives such as the Genomics England (Department of Health, UK) genome sequencing project that combine rare disease clinical phenotyping with genomics, with the aim of defining the vast majority of rare disease genes in patients as well as modifying risks and pharmacogenomics factors.

PMID: 27588584 [PubMed - as supplied by publisher]

Advancing Systems Biology in the International Conference on Intelligent Biology and Medicine (ICIBM) 2015.

BMC Syst Biol. 2016;10 Suppl 3:61

Authors: Zhao Z, Liu Y, Huang Y, Huang K, Ruan J

Abstract
The 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) was held on November 13-15, 2015 in Indianapolis, Indiana, USA. ICIBM 2015 included eight scientific sessions, three tutorial sessions, one poster session, and four keynote presentations that covered the frontier research in broad areas related to bioinformatics, systems biology, big data science, biomedical informatics, pharmacogenomics, and intelligent computing. Here, we present a summary of the 10 research articles that were selected from ICIBM 2015 and included in the supplement to BMC Systems Biology.

PMID: 27587087 [PubMed - in process]

Use of large-scale veterinary data for the investigation of antimicrobial prescribing practices in equine medicine.

Equine Vet J. 2016 Sep 2;

Authors: Welsh CE, Parkin TD, Marshall JF

Abstract
BACKGROUND: As antimicrobial resistant bacterial strains continue to emerge and spread in human and animal populations, understanding prescription practices is key in benchmarking current performance and setting goals. Antimicrobial prescription in companion veterinary species is widespread, but is neither monitored nor restricted in the US and Canada. The veterinary use of certain antimicrobial classes is discouraged in some countries, in the hope of preserving efficacy for serious human infections.
OBJECTIVES: The aim of this study was to ascertain the rate of prescription of a number of 'reserved' antimicrobials in a first-opinion US and Canadian horse cohort, and identify trends in their empirical use.
STUDY DESIGN: Retrospective cohort study.
METHODS: A large convenience sample of electronic medical records (2006 to 2012) were interrogated using text mining to identify enrofloxacin, clarithromycin and ceftiofur prescriptions. Time series analysis and logistic regression were used to identify trends and risk factors for prescription.
RESULTS: Prescription of these antimicrobials as a first-line intervention, without culture and sensitivity testing, was common in this population. Enrofloxacin prescriptions were found to increase over the study period, and there was evidence of either a reducing, or static trend in the proportion of reserved antimicrobial prescriptions informed by culture and sensitivity testing.
MAIN LIMITATIONS: Dose adequacy could not be included due to the nature of the data used.
CONCLUSIONS: Empirical use of reserved antimicrobials was common in this population, and further advice and guidance should be issued to first-opinion veterinarians to safeguard antimicrobial efficacy. This article is protected by copyright. All rights reserved.

PMID: 27589226 [PubMed - as supplied by publisher]

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature.

BMC Syst Biol. 2016;10 Suppl 3:67

Authors: Zhang Y, Wu HY, Xu J, Wang J, Soysal E, Li L, Xu H

Abstract
BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs.
RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively.
CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.

PMID: 27585838 [PubMed - in process]

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/09/02

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/09/02

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Designing a patient monitoring system for bipolar disorder using Semantic Web technologies.

Conf Proc IEEE Eng Med Biol Soc. 2015;2015:6788-91

Authors: Thermolia C, Bei ES, Petrakis EG, Kritsotakis V, Tsiknakis M, Sakkalis V

Abstract
The new movement to personalize treatment plans and improve prediction capabilities is greatly facilitated by intelligent remote patient monitoring and risk prevention. This paper focuses on patients suffering from bipolar disorder, a mental illness characterized by severe mood swings. We exploit the advantages of Semantic Web and Electronic Health Record Technologies to develop a patient monitoring platform to support clinicians. Relying on intelligently filtering of clinical evidence-based information and individual-specific knowledge, we aim to provide recommendations for treatment and monitoring at appropriate time or concluding into alerts for serious shifts in mood and patients' non response to treatment.

PMID: 26737852 [PubMed - indexed for MEDLINE]

A Comprehensive Systems Biology Approach to Studying Zika Virus.

PLoS One. 2016;11(9):e0161355

Authors: May M, Relich RF

Abstract
Zika virus (ZIKV) is responsible for an ongoing and intensifying epidemic in the Western Hemisphere. We examined the complete predicted proteomes, glycomes, and selectomes of 33 ZIKV strains representing temporally diverse members of the African lineage, the Asian lineage, and the current outbreak in the Americas. Derivation of the complete selectome is an 'omics' approach to identify distinct evolutionary pressures acting on different features of an organism. Employment of the M8 model did not show evidence of global diversifying selection acting on the ZIKV polyprotein; however, a mixed effect model of evolution showed strong evidence (P<0.05) for episodic diversifying selection acting on specific sites. Single nucleotide polymorphisms (SNPs) were predictably frequent across strains relative to the derived consensus sequence. None of the 9 published detection procedures utilize targets that share 100% identity across the 33 strains examined, indicating that ZIKV escape from molecular detection is predictable. The predicted O-linked glycome showed marked diversity across strains; however, the N-linked glycome was highly stable. All Asian and American strains examined were predicted to include glycosylation of E protein ASN154, a modification proposed to mediate neurotropism, whereas the modification was not predicted for African strains. SNP diversity, episodic diversifying selection, and differential glycosylation, particularly of ASN154, may have major biological implications for ZIKV disease. Taken together, the systems biology perspective of ZIKV indicates: a.) The recently emergent Asian/American N-glycotype is mediating the new and emerging neuropathogenic potential of ZIKV; and b.) further divergence at specific sites is predictable as endemnicity is established in the Americas.

PMID: 27584813 [PubMed - as supplied by publisher]

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation.

PLoS One. 2016;11(9):e0159902

Authors: Zimmer C

Abstract
BACKGROUND: Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models.
METHODS: The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique.
RESULTS: The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models.

PMID: 27583802 [PubMed - as supplied by publisher]

CYR61 and TAZ Upregulation and Focal Epithelial to Mesenchymal Transition May Be Early Predictors of Barrett's Esophagus Malignant Progression.

PLoS One. 2016;11(9):e0161967

Authors: Cardoso J, Mesquita M, Dias Pereira A, Bettencourt-Dias M, Chaves P, Pereira-Leal JB

Abstract
Barrett's esophagus is the major risk factor for esophageal adenocarcinoma. It has a low but non-neglectable risk, high surveillance costs and no reliable risk stratification markers. We sought to identify early biomarkers, predictive of Barrett's malignant progression, using a meta-analysis approach on gene expression data. This in silico strategy was followed by experimental validation in a cohort of patients with extended follow up from the Instituto Português de Oncologia de Lisboa de Francisco Gentil EPE (Portugal). Bioinformatics and systems biology approaches singled out two candidate predictive markers for Barrett's progression, CYR61 and TAZ. Although previously implicated in other malignancies and in epithelial-to-mesenchymal transition phenotypes, our experimental validation shows for the first time that CYR61 and TAZ have the potential to be predictive biomarkers for cancer progression. Experimental validation by reverse transcriptase quantitative PCR and immunohistochemistry confirmed the up-regulation of both genes in Barrett's samples associated with high-grade dysplasia/adenocarcinoma. In our cohort CYR61 and TAZ up-regulation ranged from one to ten years prior to progression to adenocarcinoma in Barrett's esophagus index samples. Finally, we found that CYR61 and TAZ over-expression is correlated with early focal signs of epithelial to mesenchymal transition. Our results highlight both CYR61 and TAZ genes as potential predictive biomarkers for stratification of the risk for development of adenocarcinoma and suggest a potential mechanistic route for Barrett's esophagus neoplastic progression.

PMID: 27583562 [PubMed - as supplied by publisher]

An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study.

F1000Res. 2016;5:1574

Authors: Wang Z, Ma'ayan A

Abstract
RNA-seq analysis is becoming a standard method for global gene expression profiling. However, open and standard pipelines to perform RNA-seq analysis by non-experts remain challenging due to the large size of the raw data files and the hardware requirements for running the alignment step. Here we introduce a reproducible open source RNA-seq pipeline delivered as an IPython notebook and a Docker image. The pipeline uses state-of-the-art tools and can run on various platforms with minimal configuration overhead. The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. We apply the pipeline to a recently published RNA-seq dataset collected from human neuronal progenitors infected with the Zika virus (ZIKV). In addition to confirming the presence of cell cycle genes among the genes that are downregulated by ZIKV, our analysis uncovers significant overlap with upregulated genes that when knocked out in mice induce defects in brain morphology. This result potentially points to the molecular processes associated with the microcephaly phenotype observed in newborns from pregnant mothers infected with the virus. In addition, our analysis predicts small molecules that can either mimic or reverse the expression changes induced by ZIKV. The IPython notebook and Docker image are freely available at:  http://nbviewer.jupyter.org/github/maayanlab/Zika-RNAseq-Pipeline/blob/master/Zika.ipynb and  https://hub.docker.com/r/maayanlab/zika/.

PMID: 27583132 [PubMed]

Dynamic optimization of biological networks under parametric uncertainty.

BMC Syst Biol. 2016;10(1):86

Authors: Nimmegeers P, Telen D, Logist F, Impe JV

Abstract
BACKGROUND: Micro-organisms play an important role in various industrial sectors (including biochemical, food and pharmaceutical industries). A profound insight in the biochemical reactions inside micro-organisms enables an improved biochemical process control. Biological networks are an important tool in systems biology for incorporating microscopic level knowledge. Biochemical processes are typically dynamic and the cells have often more than one objective which are typically conflicting, e.g., minimizing the energy consumption while maximizing the production of a specific metabolite. Therefore multi-objective optimization is needed to compute trade-offs between those conflicting objectives. In model-based optimization, one of the inherent problems is the presence of uncertainty. In biological processes, this uncertainty can be present due to, e.g., inherent biological variability. Not taking this uncertainty into account, possibly leads to the violation of constraints and erroneous estimates of the actual objective function(s). To account for the variance in model predictions and compute a prediction interval, this uncertainty should be taken into account during process optimization. This leads to a challenging optimization problem under uncertainty, which requires a robustified solution.
RESULTS: Three techniques for uncertainty propagation: linearization, sigma points and polynomial chaos expansion, are compared for the dynamic optimization of biological networks under parametric uncertainty. These approaches are compared in two case studies: (i) a three-step linear pathway model in which the accumulation of intermediate metabolites has to be minimized and (ii) a glycolysis inspired network model in which a multi-objective optimization problem is considered, being the minimization of the enzymatic cost and the minimization of the end time before reaching a minimum extracellular metabolite concentration. A Monte Carlo simulation procedure has been applied for the assessment of the constraint violations. For the multi-objective case study one Pareto point has been considered for the assessment of the constraint violations. However, this analysis can be performed for any Pareto point.
CONCLUSIONS: The different uncertainty propagation strategies each offer a robustified solution under parametric uncertainty. When making the trade-off between computation time and the robustness of the obtained profiles, the sigma points and polynomial chaos expansion strategies score better in reducing the percentage of constraint violations. This has been investigated for a normal and a uniform parametric uncertainty distribution. The polynomial chaos expansion approach allows to directly take prior knowledge of the parametric uncertainty distribution into account.

PMID: 27580913 [PubMed - in process]

Meta-generalis: A novel method for structuring information from radiology reports.

Appl Clin Inform. 2016;7(3):803-16

Authors: Barbosa F, Traina AJ, Muglia VF

Abstract
BACKGROUND: A structured report for imaging exams aims at increasing the precision in information retrieval and communication between physicians. However, it is more concise than free text and may limit specialists' descriptions of important findings not covered by pre-defined structures. A computational ontological structure derived from free texts designed by specialists may be a solution for this problem. Therefore, the goal of our study was to develop a methodology for structuring information in radiology reports covering specifications required for the Brazilian Portuguese language, including the terminology to be used.
METHODS: We gathered 1,701 radiological reports of magnetic resonance imaging (MRI) studies of the lumbosacral spine from three different institutions. Techniques of text mining and ontological conceptualization of lexical units extracted were used to structure information. Ten radiologists, specialists in lumbosacral MRI, evaluated the textual superstructure and terminology extracted using an electronic questionnaire.
RESULTS: The established methodology consists of six steps: 1) collection of radiology reports of a specific MRI examination; 2) textual decomposition; 3) normalization of lexical units; 4) identification of textual superstructures; 5) conceptualization of candidate-terms; and 6) evaluation of superstructures and extracted terminology by experts using an electronic questionnaire. Three different textual superstructures were identified, with terminological variations in the names of their textual categories. The number of candidate-terms conceptualized was 4,183, yielding 727 concepts. There were a total of 13,963 relationships between candidate-terms and concepts and 789 relationships among concepts.
CONCLUSIONS: The proposed methodology allowed structuring information in a more intuitive and practical way. Indications of three textual superstructures, extraction of lexicon units and the normalization and ontologically conceptualization were achieved while maintaining references to their respective categories and free text radiology reports.

PMID: 27580980 [PubMed - in process]

15 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/09/01

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

8 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/09/01

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Expression of DRD2 is Increased in Human Pancreatic Ductal Adenocarcinoma and Inhibitors Slow Tumor Growth in Mice.

Gastroenterology. 2016 Aug 28;

Authors: Jandaghi P, Najafabadi HS, Bauer AS, Papadakis AI, Fassan M, Hall A, Monast A, von Knebel Doeberitz M, Neoptolemos JP, Costello E, Greenhalf W, Scarpa A, Sipos B, Auld D, Lathrop M, Park M, Büchler MW, Strobel O, Hackert T, Giese NA, Zogopoulos G, Sangwan V, Huang S, Riazalhosseini Y, Hoheisel JD

Abstract
BACKGROUND & AIMS: Incidence of and mortality from pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, are almost equivalent, so better treatments are needed. We studied gene expression profiles of PDACs and the functions of genes with altered expression to identify new therapeutic targets.
METHODS: We performed microarray analysis to analyze gene expression profiles of 195 PDAC and 41 non-tumor pancreatic tissue samples. We undertook an extensive analysis of PDAC transcriptome by superimposing interaction networks of proteins encoded by aberrantly expressed genes over signaling pathways associated with PDAC development to identify factors that might alter regulation of these pathways during tumor progression. We performed tissue microarray analysis to verify changes in expression of candidate protein using an independent set of 152 samples (40 non-tumor pancreatic tissues, 63 PDAC sections, and 49 chronic pancreatitis samples). We validated the functional relevance of the candidate molecule using RNA interference (RNAi) or pharmacologic inhibitors in pancreatic cancer cell lines and analyses of xenograft tumors in mice.
RESULTS: In an analysis of 38,276 human genes and loci, we identified 1676 genes that were significantly upregulated and 1166 genes that were significantly downregulated in PDAC, compared with non-tumor pancreatic tissues. One gene that was upregulated and associated with multiple signaling pathways that are dysregulated in PDAC was G protein subunit alpha i2 (GNAI2), which has not been previously associated with PDAC. GNAI2 mediates the effects of dopamine receptor D2 (DRD2) on cAMP-signaling; PDAC tissues had a slight but significant increase in DRD2 mRNA. Levels of DRD2 protein were substantially increased in PDACs, compared with non-tumor tissues, in tissue microarray analyses. RNAi knockdown of DRD2 or inhibition with pharmacologic antagonists (pimozide and haloperidol) reduced proliferation of pancreatic cancer cells, induced endoplasmic reticulum stress and apoptosis, and reduced cell migration. RNAi knockdown of DRD2 in pancreatic tumor cells reduced growth of xenograft tumors in mice, and administration of the DRD2 inhibitor haloperidol to mice with orthotopic xenograft tumors reduced final tumor size and metastasis.
CONCLUSIONS: In gene expression profile analysis of PDAC samples, we found the DRD2 signaling pathway to be activated. Inhibition of DRD2 in pancreatic cancer cells reduced proliferation and migration, and slowed growth of xenograft tumors in mice. DRD2 antagonists routinely used for management of schizophrenia might tested in patients with pancreatic cancer.

PMID: 27578530 [PubMed - as supplied by publisher]

Fusing literature and full network data improves disease similarity computation.

BMC Bioinformatics. 2016;17(1):326

Authors: Li P, Nie Y, Yu J

Abstract
BACKGROUND: Identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. A number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. Most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. In this study, we propose a new method (MedNetSim) for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature.
RESULTS: Among function-based methods, NetSim achieved the best performance. Its average AUC (area under the receiver operating characteristic curve) reached 95.2 %. MedSim, whose performance was even comparable to some function-based methods, acquired the highest average AUC in all semantic-based methods. Integration of MedSim and NetSim (MedNetSim) further improved the average AUC to 96.4 %. We further studied the effectiveness of different data sources. It was found that quality of protein interaction data was more important than its volume. On the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. Utilizing higher volume of disease-related gene data further improved the average AUC of MedNetSim and NetSim to 97.5 % and 96.7 %, respectively.
CONCLUSIONS: Integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. Lacking sufficient disease-related gene data, literature-based methods such as MedSim can be a great addition to function-based algorithms. It may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. Disease similarities can be computed using the proposed methods at http:// www.digintelli.com:8000/ .

PMID: 27578323 [PubMed - in process]

Clinically Available Medicines Demonstrating Anti-Toxoplasma Activity.

Antimicrob Agents Chemother. 2015 Dec;59(12):7161-9

Authors: Neville AJ, Zach SJ, Wang X, Larson JJ, Judge AK, Davis LA, Vennerstrom JL, Davis PH

Abstract
Toxoplasma gondii is an apicomplexan parasite of humans and other mammals, including livestock and companion animals. While chemotherapeutic regimens, including pyrimethamine and sulfadiazine regimens, ameliorate acute or recrudescent disease such as toxoplasmic encephalitis or ocular toxoplasmosis, these drugs are often toxic to the host. Moreover, no approved options are available to treat infected women who are pregnant. Lastly, no drug regimen has shown the ability to eradicate the chronic stage of infection, which is characterized by chemoresistant intracellular cysts that persist for the life of the host. In an effort to promote additional chemotherapeutic options, we now evaluate clinically available drugs that have shown efficacy in disease models but which lack clinical case reports. Ideally, less-toxic treatments for the acute disease can be identified and developed, with an additional goal of cyst clearance from human and animal hosts.

PMID: 26392504 [PubMed - indexed for MEDLINE]

Dynamic User Interfaces for Service Oriented Architectures in Healthcare.

Stud Health Technol Inform. 2016;228:795-7

Authors: Schweitzer M, Hoerbst A

Abstract
Electronic Health Records (EHRs) play a crucial role in healthcare today. Considering a data-centric view, EHRs are very advanced as they provide and share healthcare data in a cross-institutional and patient-centered way adhering to high syntactic and semantic interoperability. However, the EHR functionalities available for the end users are rare and hence often limited to basic document query functions. Future EHR use necessitates the ability to let the users define their needed data according to a certain situation and how this data should be processed. Workflow and semantic modelling approaches as well as Web services provide means to fulfil such a goal. This thesis develops concepts for dynamic interfaces between EHR end users and a service oriented eHealth infrastructure, which allow the users to design their flexible EHR needs, modeled in a dynamic and formal way. These are used to discover, compose and execute the right Semantic Web services.

PMID: 27577496 [PubMed - in process]

A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medications.

Stud Health Technol Inform. 2016;228:519-23

Authors: Abidi SR, Cox J, Abusharekh A, Hashemian N, Abidi SS

Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy. In Canada, NOAC medications are not directly available to patients who belong to provincial benefits programs, rather a NOAC special authorization process establishes the eligibility of a patient to receive a NOAC and be paid by the provincial Pharmacare program. This special authorization process is tedious and paper-based which inhibits physicians to prescribe NOAC leading to suboptimal AF care to patients. In this paper, we present a computerized NOAC Authorization Decision Support System (NOAC-ADSS), accessible to physicians to help them (a) determine a patient eligibility for NOAC based on Canadian AF clinical guidelines, and (b) complete the special authorization form. We present a semantic web based system to ontologically model the NOAC eligibility criteria and execute the knowledge to determine a patient NOAC eligibility and dosage.

PMID: 27577437 [PubMed - in process]