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GP pilots are "last chance saloon" for care.data scheme.

BMJ (Clinical research ed.)

O'Dowd A.
PMID: 25613568
BMJ. 2015 Jan 22;350:h419. doi: 10.1136/bmj.h419.

No abstract available.

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O'Dowd A. GP pilots are "last chance saloon" for care.data scheme. BMJ. 2015;350:h419doi: 10.1136/bmj.h419.
O'Dowd, A. (2015). GP pilots are "last chance saloon" for care.data scheme. BMJ (Clinical research ed.), 350h419. https://doi.org/10.1136/bmj.h419
O'Dowd, Adrian. "GP pilots are "last chance saloon" for care.data scheme." BMJ (Clinical research ed.) vol. 350 (2015): h419. doi: https://doi.org/10.1136/bmj.h419
O'Dowd A. GP pilots are "last chance saloon" for care.data scheme. BMJ. 2015 Jan 22;350:h419. doi: 10.1136/bmj.h419. PMID: 25613568.
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The Social Life of Data.

Neuroinformatics

Kennedy DN.
PMID: 26942593
Neuroinformatics. 2016 Apr;14(2):129-30. doi: 10.1007/s12021-016-9298-5.

No abstract available.

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Kennedy DN. The Social Life of Data. Neuroinformatics. 2016;14(2):129-30doi: 10.1007/s12021-016-9298-5.
Kennedy, D. N. (2016). The Social Life of Data. Neuroinformatics, 14(2), 129-30. https://doi.org/10.1007/s12021-016-9298-5
Kennedy, David N. "The Social Life of Data." Neuroinformatics vol. 14,2 (2016): 129-30. doi: https://doi.org/10.1007/s12021-016-9298-5
Kennedy DN. The Social Life of Data. Neuroinformatics. 2016 Apr;14(2):129-30. doi: 10.1007/s12021-016-9298-5. PMID: 26942593.
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The future is in the numbers: the power of predictive analysis in the biomedical educational environment.

Medical education online

Gullo CA.
PMID: 27374246
Med Educ Online. 2016 Jul 01;21:32516. doi: 10.3402/meo.v21.32516. eCollection 2016.

Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to...

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Gullo CA. The future is in the numbers: the power of predictive analysis in the biomedical educational environment. Med Educ Online. 2016;21:32516doi: 10.3402/meo.v21.32516.
Gullo, C. A. (2016). The future is in the numbers: the power of predictive analysis in the biomedical educational environment. Medical education online, 2132516. https://doi.org/10.3402/meo.v21.32516
Gullo, Charles A. "The future is in the numbers: the power of predictive analysis in the biomedical educational environment." Medical education online vol. 21 (2016): 32516. doi: https://doi.org/10.3402/meo.v21.32516
Gullo CA. The future is in the numbers: the power of predictive analysis in the biomedical educational environment. Med Educ Online. 2016 Jul 01;21:32516. doi: 10.3402/meo.v21.32516. eCollection 2016. PMID: 27374246; PMCID: PMC4931024.
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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

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Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database.

Studies in health technology and informatics

Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M.
PMID: 26262371
Stud Health Technol Inform. 2015;216:1072.

The aim of this study was to investigate whether standard Big Data mining methods lead to clinically useful results. An association analysis was performed using the apriori algorithm to discover associations among co-morbidities of diabetes patients. Selected data were...

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Nienhoff H, Huebner U, Frey A, et al. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072
Nienhoff, H., Huebner, U., Frey, A., Przysucha, M., & Kimura, M. (2015). Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Studies in health technology and informatics, 2161072.
Nienhoff, Hendrik, et al. "Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database." Studies in health technology and informatics vol. 216 (2015): 1072.
Nienhoff H, Huebner U, Frey A, Przysucha M, Kimura M. Restricted Versus Unrestricted Search Space: Experience from Mining a Large Japanese Database. Stud Health Technol Inform. 2015;216:1072. PMID: 26262371.
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Statistical learning and selective inference.

Proceedings of the National Academy of Sciences of the United States of America

Taylor J, Tibshirani RJ.
PMID: 26100887
Proc Natl Acad Sci U S A. 2015 Jun 23;112(25):7629-34. doi: 10.1073/pnas.1507583112.

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for...

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Taylor J, Tibshirani RJ. Statistical learning and selective inference. Proc Natl Acad Sci U S A. 2015;112(25):7629-34doi: 10.1073/pnas.1507583112.
Taylor, J., & Tibshirani, R. J. (2015). Statistical learning and selective inference. Proceedings of the National Academy of Sciences of the United States of America, 112(25), 7629-34. https://doi.org/10.1073/pnas.1507583112
Taylor, Jonathan, and Tibshirani, Robert J. "Statistical learning and selective inference." Proceedings of the National Academy of Sciences of the United States of America vol. 112,25 (2015): 7629-34. doi: https://doi.org/10.1073/pnas.1507583112
Taylor J, Tibshirani RJ. Statistical learning and selective inference. Proc Natl Acad Sci U S A. 2015 Jun 23;112(25):7629-34. doi: 10.1073/pnas.1507583112. PMID: 26100887; PMCID: PMC4485109.
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Using Drug Similarities for Discovery of Possible Adverse Reactions.

AMIA ... Annual Symposium proceedings. AMIA Symposium

Muñoz E, Nováček V, Vandenbussche PY.
PMID: 28269889
AMIA Annu Symp Proc. 2017 Feb 10;2016:924-933. eCollection 2016.

We propose a new computational method for discovery of possible adverse drug reactions. The method consists of two key steps. First we use openly available resources to semi-automatically compile a consolidated data set describing drugs and their features (e.g.,...

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Muñoz E, Nováček V, Vandenbussche PY. Using Drug Similarities for Discovery of Possible Adverse Reactions. AMIA Annu Symp Proc. 2017;2016:924-933
Muñoz, E., Nováček, V., & Vandenbussche, P. Y. (2016). Using Drug Similarities for Discovery of Possible Adverse Reactions. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016924-933.
Muñoz, Emir, et al. "Using Drug Similarities for Discovery of Possible Adverse Reactions." AMIA ... Annual Symposium proceedings. AMIA Symposium vol. 2016 (2016): 924-933.
Muñoz E, Nováček V, Vandenbussche PY. Using Drug Similarities for Discovery of Possible Adverse Reactions. AMIA Annu Symp Proc. 2017 Feb 10;2016:924-933. eCollection 2016. PMID: 28269889; PMCID: PMC5333276.
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Visualization of Order Set Creation and Usage Patterns in Early Implementation Phases of an Electronic Health Record.

AMIA ... Annual Symposium proceedings. AMIA Symposium

Hulse NC, Lee J, Borgeson T.
PMID: 28269862
AMIA Annu Symp Proc. 2017 Feb 10;2016:657-666. eCollection 2016.

Robust order set catalogs are considered to be a vital part of a computerized physician order entry (CPOE) implementation. Tools and processes for building, localizing, and maintaining these content sets in a centralized repository are important in facilitating the...

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Hulse NC, Lee J, Borgeson T. Visualization of Order Set Creation and Usage Patterns in Early Implementation Phases of an Electronic Health Record. AMIA Annu Symp Proc. 2017;2016:657-666
Hulse, N. C., Lee, J., & Borgeson, T. (2016). Visualization of Order Set Creation and Usage Patterns in Early Implementation Phases of an Electronic Health Record. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016657-666.
Hulse, Nathan C, et al. "Visualization of Order Set Creation and Usage Patterns in Early Implementation Phases of an Electronic Health Record." AMIA ... Annual Symposium proceedings. AMIA Symposium vol. 2016 (2016): 657-666.
Hulse NC, Lee J, Borgeson T. Visualization of Order Set Creation and Usage Patterns in Early Implementation Phases of an Electronic Health Record. AMIA Annu Symp Proc. 2017 Feb 10;2016:657-666. eCollection 2016. PMID: 28269862; PMCID: PMC5333265.
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MOST WIRED. Creating health care value with big data intelligence.

Hospitals & health networks

Hoppszallern S.
PMID: 26904908
Hosp Health Netw. 2016 Jan;90(1):20.

No abstract available.

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Hoppszallern S. MOST WIRED. Creating health care value with big data intelligence. Hosp Health Netw. 2016;90(1):20
Hoppszallern, S. (2016). MOST WIRED. Creating health care value with big data intelligence. Hospitals & health networks, 90(1), 20.
Hoppszallern, Suzanna. "MOST WIRED. Creating health care value with big data intelligence." Hospitals & health networks vol. 90,1 (2016): 20.
Hoppszallern S. MOST WIRED. Creating health care value with big data intelligence. Hosp Health Netw. 2016 Jan;90(1):20. PMID: 26904908.
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Patient-generated health data foundation for personalized collaborative care.

Computers, informatics, nursing : CIN

Hull S.
PMID: 25974360
Comput Inform Nurs. 2015 May;33(5):177-80. doi: 10.1097/CIN.0000000000000159.

No abstract available.

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Hull S. Patient-generated health data foundation for personalized collaborative care. Comput Inform Nurs. 2015;33(5):177-80doi: 10.1097/CIN.0000000000000159.
Hull, S. (2015). Patient-generated health data foundation for personalized collaborative care. Computers, informatics, nursing : CIN, 33(5), 177-80. https://doi.org/10.1097/CIN.0000000000000159
Hull, Susan. "Patient-generated health data foundation for personalized collaborative care." Computers, informatics, nursing : CIN vol. 33,5 (2015): 177-80. doi: https://doi.org/10.1097/CIN.0000000000000159
Hull S. Patient-generated health data foundation for personalized collaborative care. Comput Inform Nurs. 2015 May;33(5):177-80. doi: 10.1097/CIN.0000000000000159. PMID: 25974360.
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Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment.

BMC bioinformatics

Hornung R, Boulesteix AL, Causeur D.
PMID: 26753519
BMC Bioinformatics. 2016 Jan 12;17:27. doi: 10.1186/s12859-015-0870-z.

BACKGROUND: In the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups; for example, measured at different times, under different conditions or even in different labs. These groups are...

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Hornung R, Boulesteix AL, Causeur D. Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics. 2016;17:27doi: 10.1186/s12859-015-0870-z.
Hornung, R., Boulesteix, A. L., & Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC bioinformatics, 1727. https://doi.org/10.1186/s12859-015-0870-z
Hornung, Roman, et al. "Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment." BMC bioinformatics vol. 17 (2016): 27. doi: https://doi.org/10.1186/s12859-015-0870-z
Hornung R, Boulesteix AL, Causeur D. Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics. 2016 Jan 12;17:27. doi: 10.1186/s12859-015-0870-z. PMID: 26753519; PMCID: PMC4710051.
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Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification.

PloS one

Pongor LS, Vera R, Ligeti B.
PMID: 25077800
PLoS One. 2014 Jul 31;9(7):e103441. doi: 10.1371/journal.pone.0103441. eCollection 2014.

Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result,...

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Pongor LS, Vera R, Ligeti B. Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification. PLoS One. 2014;9(7):e103441doi: 10.1371/journal.pone.0103441.
Pongor, L. S., Vera, R., & Ligeti, B. (2014). Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification. PloS one, 9(7), e103441. https://doi.org/10.1371/journal.pone.0103441
Pongor, Lőrinc S, et al. "Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification." PloS one vol. 9,7 (2014): e103441. doi: https://doi.org/10.1371/journal.pone.0103441
Pongor LS, Vera R, Ligeti B. Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification. PLoS One. 2014 Jul 31;9(7):e103441. doi: 10.1371/journal.pone.0103441. eCollection 2014. PMID: 25077800; PMCID: PMC4117525.
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Showing 1 to 12 of 180 entries