We observed a marginal inverse correlation between the EndoPAT score and plasma levels of ADMA (r = -0.364). : Ontology-driven indexing of public datasets for translational bioinformatics. We validated some of the usage patterns learned from the data against sources of known on-label and off-label use. Developing a delivery science for artificial intelligence in healthcare. Cure, O. C., Maurer, H., Shah, N. H., Le Pendu, P. Detecting unplanned care from clinician notes in electronic health records. Integration and publication of heterogeneous text-mined relationships on the Semantic Web. Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network. Nikfarjam, A., Ransohoff, J. D., Callahan, A., Jones, E., Loew, B., Kwong, B. Y., Sarin, K. Y., Shah, N. H. It is time to learn from patients like mine. View details for DOI 10.1186/1471-2105-7-196, View details for Web of Science ID 000239302400001, View details for PubMedCentralID PMC1522024. The Lexicon Builder Web service: Building Custom Lexicons from two hundred Biomedical Ontologies. One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. George Hripcsak, Michel Dumontier and Nigam H. Shah. As a result, historical values of these physiological measures for a population sample can be used to characterize disease progression patterns. After a 2-week washout period, participants were crossed over to receive the alternate treatment for the ensuing 4 weeks. Low, Y. S., Daugherty, A. C., Schroeder, E. A., Chen, W., Seto, T., Weber, S., Lim, M., Hastie, T., Mathur, M., Desai, M., Farrington, C., Radin, A. Making Machine Learning Models Clinically Useful. View details for DOI 10.1186/2041-1480-2-S1-S3. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use.We present the Biomedical Ontology Recommender web service. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. We conducted a controlled, open-label, cross-over pilot study among 21 adults aged 18 and older who are healthy (n=11) or have established clinical cardiovascular disease (n=10). Project: Continuously profiling patients screened for … Improving Palliative Care with Deep Learning. endstream
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The objective of this study was to test the feasibility and accuracy of identifying patient centered outcomes within the EHR.Data from patients with localized prostate cancer undergoing prostatectomy were used to develop and test algorithms to accurately identify patient-centered outcomes in post-operative EHRs - we used urinary incontinence as the use case. Nead, K. T., Gaskin, G., Chester, C., Swisher-McClure, S., Dudley, J. T., Leeper, N. J., Shah, N. H. Association Between Androgen Deprivation Therapy and Risk of Dementia. This paper presents the Open Biomedical Annotator (OBA), an ontology-based Web service that annotates public datasets with biomedical ontology concepts based on their textual metadata (www.bioontology.org). Agarwal, V., Han, L., Madan, I., Saluja, S., Shidham, A., Shah, N. H. LEARNING ATTRIBUTES OF DISEASE PROGRESSION FROM TRAJECTORIES OF SPARSE LAB VALUES. We conducted a large-scale study of Web search log data gathered during 2010. For example, protein-protein interaction networks have been successfully used to identify candidate disease genes. Stanford University pathologists, researchers and their collaborators worldwide use TMAD for designing, viewing, scoring and analyzing their tissue microarrays. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods.Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. In this paper, we present a comparison of two concept recognizers - NLM's MetaMap and the University of Michigan's Mgrep. Curriculum Vitae Nigam H. Shah Page 1 of 11 Last Updated 8/4/2011 CURRICULUM VITAE NIGAM H. SHAH, MBBS, PhD OFFICE ADDRESS: Stanford Center for Biomedical Informatics Research Medical School Office Building X229 1265 Welch Road Stanford, CA 94305-5479 Jung, K., LePendu, P., Chen, W. S., Iyer, S. V., Readhead, B., Dudley, J. T., Shah, N. H. Building the graph of medicine from millions of clinical narratives. Pannu, J., Poole, S., Shah, N., Shah, N. H. Enhanced Quality Measurement Event Detection: An Application to Physician Reporting. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. Vascular Medicine 2015 20: 4, 309-316 ... (CV) risk. The development of reference intervals using traditional methods is time consuming and costly. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics "under the hood. It is currently being evaluated for several use cases to establish its utility in biomedical information processing tasks. View details for DOI 10.1186/2041-1480-3-S1-I1. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and use ontologies and terminologies in biomedicine. View details for PubMedCentralID PMC6457095. We find that SDAs can attain reasonable predictive accuracy in signaling adverse events. ", View details for DOI 10.1016/j.websem.2011.06.005, View details for Web of Science ID 000300169800007, View details for PubMedCentralID PMC3170774. Try the Course for Free. We have previously developed methods to map text-annotations of tissue microarrays to concepts in the NCI thesaurus and SNOMED-CT. We have shown that the syntactic and frequency information is useful to identify errors in the Metathesaurus. View details for Web of Science ID 000306925000007. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Ross, E. G., Jung, K., Dudley, J. T., Li, L., Leeper, N. J., Shah, N. H. Predicting Need for Advanced Illness or Palliative Care In A Primary Care Population Using Electronic Health Record Data. A reference standard of over 600 established and plausible ADRs was created and used to evaluate the proposed approach against a comparator.The combined signaling system achieved a statistically significant large improvement over AERS (baseline) in the precision of top ranked signals. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. View details for Web of Science ID 000251864600008. A., Fleming, S. L., Wilfley, D. E., Terence Wilson, G., Milstein, A., Jurafsky, D., Arnow, B. We theorized that those potential associations for which there is evidence from multiple complementary sources are more likely to be true, and explored this idea using a published database of drug-drug-adverse event associations derived from electronic health records (EHRs).We prioritized drug-drug-event associations derived from EHRs using four sources of information: (1) public databases, (2) sources of spontaneous reports, (3) literature, and (4) non-EHR drug-drug interaction (DDI) prediction methods. Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Characterizing treatment pathways at scale using the OHDSI network. These data provide an example of how a combination of experimental studies and data-mining approaches can be applied to prioritize drug safety signals for further investigation.