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ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow.

International journal of biomedical data mining

Borthwick KM, Smelser DT, Bock JA, Elmore JR, Ryer EJ, Ye Z, Pacheco JA, Carrell DS, Michalkiewicz M, Thompson WK, Pathak J, Bielinski SJ, Denny JC, Linneman JG, Peissig PL, Kho AN, Gottesman O, Parmar H, Kullo IJ, McCarty CA, Böttinger EP, Larson EB, Jarvik GP, Harley JB, Bajwa T, Franklin DP, Carey DJ, Kuivaniemi H, Tromp G.
PMID: 27054044
Int J Biomed Data Min. 2015 Dec;4(1). Epub 2015 Jul 30.

BACKGROUND AND OBJECTIVE: We designed an algorithm to identify abdominal aortic aneurysm cases and controls from electronic health records to be shared and executed within the "electronic Medical Records and Genomics" (eMERGE) Network.MATERIALS AND METHODS: Structured Query Language, was...

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Borthwick KM, Smelser DT, Bock JA, et al. ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow. Int J Biomed Data Min. 2015;4(1)
Borthwick, K. M., Smelser, D. T., Bock, J. A., Elmore, J. R., Ryer, E. J., Ye, Z., Pacheco, J. A., Carrell, D. S., Michalkiewicz, M., Thompson, W. K., Pathak, J., Bielinski, S. J., Denny, J. C., Linneman, J. G., Peissig, P. L., Kho, A. N., Gottesman, O., Parmar, H., Kullo, I. J., McCarty, C. A., Böttinger, E. P., Larson, E. B., Jarvik, G. P., Harley, J. B., Bajwa, T., Franklin, D. P., Carey, D. J., Kuivaniemi, H., & Tromp, G. (2015). ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow. International journal of biomedical data mining, 4(1), .
Borthwick, Kenneth M, et al. "ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow." International journal of biomedical data mining vol. 4,1 (2015).
Borthwick KM, Smelser DT, Bock JA, Elmore JR, Ryer EJ, Ye Z, Pacheco JA, Carrell DS, Michalkiewicz M, Thompson WK, Pathak J, Bielinski SJ, Denny JC, Linneman JG, Peissig PL, Kho AN, Gottesman O, Parmar H, Kullo IJ, McCarty CA, Böttinger EP, Larson EB, Jarvik GP, Harley JB, Bajwa T, Franklin DP, Carey DJ, Kuivaniemi H, Tromp G. ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow. Int J Biomed Data Min. 2015 Dec;4(1). Epub 2015 Jul 30. PMID: 27054044; PMCID: PMC4820287.
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Practical considerations in genomic decision support: The eMERGE experience.

Journal of pathology informatics

Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Cobb BL, Denny JC, Hakonarson H, Hartzler AL, Hripcsak G, Kannry J, Kohane IS, Kullo IJ, Lin S, Manzi S, Marsolo K, Overby CL, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden DM, Tromp G, Uphoff T, Weng C, Wolf W, Williams MS, Starren J.
PMID: 26605115
J Pathol Inform. 2015 Sep 28;6:50. doi: 10.4103/2153-3539.165999. eCollection 2015.

BACKGROUND: Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized...

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Herr TM, Bielinski SJ, Bottinger E, et al. Practical considerations in genomic decision support: The eMERGE experience. J Pathol Inform. 2015;6:50doi: 10.4103/2153-3539.165999.
Herr, T. M., Bielinski, S. J., Bottinger, E., Brautbar, A., Brilliant, M., Chute, C. G., Cobb, B. L., Denny, J. C., Hakonarson, H., Hartzler, A. L., Hripcsak, G., Kannry, J., Kohane, I. S., Kullo, I. J., Lin, S., Manzi, S., Marsolo, K., Overby, C. L., Pathak, J., Peissig, P., Pulley, J., Ralston, J., Rasmussen, L., Roden, D. M., Tromp, G., Uphoff, T., Weng, C., Wolf, W., Williams, M. S., & Starren, J. (2015). Practical considerations in genomic decision support: The eMERGE experience. Journal of pathology informatics, 650. https://doi.org/10.4103/2153-3539.165999
Herr, Timothy M, et al. "Practical considerations in genomic decision support: The eMERGE experience." Journal of pathology informatics vol. 6 (2015): 50. doi: https://doi.org/10.4103/2153-3539.165999
Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Cobb BL, Denny JC, Hakonarson H, Hartzler AL, Hripcsak G, Kannry J, Kohane IS, Kullo IJ, Lin S, Manzi S, Marsolo K, Overby CL, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden DM, Tromp G, Uphoff T, Weng C, Wolf W, Williams MS, Starren J. Practical considerations in genomic decision support: The eMERGE experience. J Pathol Inform. 2015 Sep 28;6:50. doi: 10.4103/2153-3539.165999. eCollection 2015. PMID: 26605115; PMCID: PMC4629307.
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Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

BioData mining

Li R, Dudek SM, Kim D, Hall MA, Bradford Y, Peissig PL, Brilliant MH, Linneman JG, McCarty CA, Bao L, Ritchie MD.
PMID: 27168765
BioData Min. 2016 May 10;9:18. doi: 10.1186/s13040-016-0094-4. eCollection 2016.

BACKGROUND: The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With...

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Li R, Dudek SM, Kim D, et al. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network. BioData Min. 2016;9:18doi: 10.1186/s13040-016-0094-4.
Li, R., Dudek, S. M., Kim, D., Hall, M. A., Bradford, Y., Peissig, P. L., Brilliant, M. H., Linneman, J. G., McCarty, C. A., Bao, L., & Ritchie, M. D. (2016). Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network. BioData mining, 918. https://doi.org/10.1186/s13040-016-0094-4
Li, Ruowang, et al. "Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network." BioData mining vol. 9 (2016): 18. doi: https://doi.org/10.1186/s13040-016-0094-4
Li R, Dudek SM, Kim D, Hall MA, Bradford Y, Peissig PL, Brilliant MH, Linneman JG, McCarty CA, Bao L, Ritchie MD. Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network. BioData Min. 2016 May 10;9:18. doi: 10.1186/s13040-016-0094-4. eCollection 2016. PMID: 27168765; PMCID: PMC4862166.
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Factors affecting the stability and performance of ionic liquid-based planar transient photodetectors.

Langmuir : the ACS journal of surfaces and colloids

Dalgleish S, Reissig L, Hu L, Matsushita MM, Sudo Y, Awaga K.
PMID: 25895167
Langmuir. 2015 May 12;31(18):5235-43. doi: 10.1021/la504972q. Epub 2015 Apr 30.

A novel planar architecture has been developed for the study of photodetectors utilizing the transient photocurrent response induced by a metal/insulator/semiconductor/metal (MISM) structured device, where the insulator is an ionic liquid (IL-MISM). Using vanadyl 2,3-naphthalocyanine, which absorbs in the...

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Dalgleish S, Reissig L, Hu L, et al. Factors affecting the stability and performance of ionic liquid-based planar transient photodetectors. Langmuir. 2015;31(18):5235-43doi: 10.1021/la504972q.
Dalgleish, S., Reissig, L., Hu, L., Matsushita, M. M., Sudo, Y., & Awaga, K. (2015). Factors affecting the stability and performance of ionic liquid-based planar transient photodetectors. Langmuir : the ACS journal of surfaces and colloids, 31(18), 5235-43. https://doi.org/10.1021/la504972q
Dalgleish, Simon, et al. "Factors affecting the stability and performance of ionic liquid-based planar transient photodetectors." Langmuir : the ACS journal of surfaces and colloids vol. 31,18 (2015): 5235-43. doi: https://doi.org/10.1021/la504972q
Dalgleish S, Reissig L, Hu L, Matsushita MM, Sudo Y, Awaga K. Factors affecting the stability and performance of ionic liquid-based planar transient photodetectors. Langmuir. 2015 May 12;31(18):5235-43. doi: 10.1021/la504972q. Epub 2015 Apr 30. PMID: 25895167.
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Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

JMIR medical informatics

Peissig P, Schwei KM, Kadolph C, Finamore J, Cancel E, McCarty CA, Okorie A, Thomas KL, Allen Pacheco J, Pathak J, Ellis SB, Denny JC, Rasmussen LV, Tromp G, Williams MS, Vrabec TR, Brilliant MH.
PMID: 28903894
JMIR Med Inform. 2017 Sep 13;5(3):e27. doi: 10.2196/medinform.7465.

BACKGROUND: The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical.OBJECTIVE: The purpose of this study was to...

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Peissig P, Schwei KM, Kadolph C, et al. Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application. JMIR Med Inform. 2017;5(3):e27doi: 10.2196/medinform.7465.
Peissig, P., Schwei, K. M., Kadolph, C., Finamore, J., Cancel, E., McCarty, C. A., Okorie, A., Thomas, K. L., Allen Pacheco, J., Pathak, J., Ellis, S. B., Denny, J. C., Rasmussen, L. V., Tromp, G., Williams, M. S., Vrabec, T. R., & Brilliant, M. H. (2017). Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application. JMIR medical informatics, 5(3), e27. https://doi.org/10.2196/medinform.7465
Peissig, Peggy, et al. "Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application." JMIR medical informatics vol. 5,3 (2017): e27. doi: https://doi.org/10.2196/medinform.7465
Peissig P, Schwei KM, Kadolph C, Finamore J, Cancel E, McCarty CA, Okorie A, Thomas KL, Allen Pacheco J, Pathak J, Ellis SB, Denny JC, Rasmussen LV, Tromp G, Williams MS, Vrabec TR, Brilliant MH. Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application. JMIR Med Inform. 2017 Sep 13;5(3):e27. doi: 10.2196/medinform.7465. PMID: 28903894; PMCID: PMC5617903.
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The Marshfield Clinic Personalized Medicine Research Project: 2008 scientific update and lessons learned in the first 6 years.

Personalized medicine

McCarty CA, Peissig P, Caldwell MD, Wilke RA.
PMID: 29783440
Per Med. 2008 Sep;5(5):529-542. doi: 10.2217/17410541.5.5.529.

The Marshfield Clinic Personalized Medicine Research Project is the largest population-based biobank in the USA, with the ability to recontact subjects to obtain additional information to facilitate gene-environment studies. Nearly 20,000 adults have enrolled in the Personalized Medicine Research...

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McCarty CA, Peissig P, Caldwell MD, et al. The Marshfield Clinic Personalized Medicine Research Project: 2008 scientific update and lessons learned in the first 6 years. Per Med. 2008;5(5):529-542doi: 10.2217/17410541.5.5.529.
McCarty, C. A., Peissig, P., Caldwell, M. D., & Wilke, R. A. (2008). The Marshfield Clinic Personalized Medicine Research Project: 2008 scientific update and lessons learned in the first 6 years. Personalized medicine, 5(5), 529-542. https://doi.org/10.2217/17410541.5.5.529
McCarty, Catherine A, et al. "The Marshfield Clinic Personalized Medicine Research Project: 2008 scientific update and lessons learned in the first 6 years." Personalized medicine vol. 5,5 (2008): 529-542. doi: https://doi.org/10.2217/17410541.5.5.529
McCarty CA, Peissig P, Caldwell MD, Wilke RA. The Marshfield Clinic Personalized Medicine Research Project: 2008 scientific update and lessons learned in the first 6 years. Per Med. 2008 Sep;5(5):529-542. doi: 10.2217/17410541.5.5.529. PMID: 29783440.
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Pre-Erythrocytic Vaccines against Malaria.

Vaccines

Marques-da-Silva C, Peissig K, Kurup SP.
PMID: 32708179
Vaccines (Basel). 2020 Jul 21;8(3). doi: 10.3390/vaccines8030400.

Malaria, caused by the protozoan

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Marques-da-Silva C, Peissig K, Kurup SP. Pre-Erythrocytic Vaccines against Malaria. Vaccines (Basel). 2020;8(3)doi: 10.3390/vaccines8030400.
Marques-da-Silva, C., Peissig, K., & Kurup, S. P. (2020). Pre-Erythrocytic Vaccines against Malaria. Vaccines, 8(3), . https://doi.org/10.3390/vaccines8030400
Marques-da-Silva, Camila, et al. "Pre-Erythrocytic Vaccines against Malaria." Vaccines vol. 8,3 (2020). doi: https://doi.org/10.3390/vaccines8030400
Marques-da-Silva C, Peissig K, Kurup SP. Pre-Erythrocytic Vaccines against Malaria. Vaccines (Basel). 2020 Jul 21;8(3). doi: 10.3390/vaccines8030400. PMID: 32708179; PMCID: PMC7565498.
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Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks.

Proceedings of the ACM Conference on Health, Inference, and Learning

Zhang W, Peissig P, Kuang Z, Page D.
PMID: 33283213
Proc ACM Conf Health Inference Learn (2020). 2020 Apr;2020:30-39. doi: 10.1145/3368555.3384459.

Adverse drug reactions (ADRs) are detrimental and unexpected clinical incidents caused by drug intake. The increasing availability of massive quantities of longitudinal event data such as electronic health records (EHRs) has redefined ADR discovery as a big data analytics...

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Zhang W, Peissig P, Kuang Z, et al. Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks. Proc ACM Conf Health Inference Learn (2020). 2020;2020:30-39doi: 10.1145/3368555.3384459.
Zhang, W., Peissig, P., Kuang, Z., & Page, D. (2020). Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks. Proceedings of the ACM Conference on Health, Inference, and Learning, 202030-39. https://doi.org/10.1145/3368555.3384459
Zhang, Wei, et al. "Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks." Proceedings of the ACM Conference on Health, Inference, and Learning vol. 2020 (2020): 30-39. doi: https://doi.org/10.1145/3368555.3384459
Zhang W, Peissig P, Kuang Z, Page D. Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks. Proc ACM Conf Health Inference Learn (2020). 2020 Apr;2020:30-39. doi: 10.1145/3368555.3384459. PMID: 33283213; PMCID: PMC7718770.
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Speech Intelligibility Enhancement by Interaural Magnification.

Acta oto-laryngologica

Kollmeier B, Peissig J.
PMID: 31905538
Acta Otolaryngol. 1990;109:215-223. doi: 10.1080/00016489.1990.12088432.

Hearing-impaired persons suffer from loss of speech intelligibility in noisy environments as well as from a decrease in binaural performance (e.g., interaural time and intensity discrimination). In order to improve speech intelligibility for spatially separated signal and noise sources...

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Kollmeier B, Peissig J. Speech Intelligibility Enhancement by Interaural Magnification. Acta Otolaryngol. 1990;109:215-223doi: 10.1080/00016489.1990.12088432.
Kollmeier, B., & Peissig, J. (1990). Speech Intelligibility Enhancement by Interaural Magnification. Acta oto-laryngologica, 109215-223. https://doi.org/10.1080/00016489.1990.12088432
Kollmeier, B, and Peissig, J. "Speech Intelligibility Enhancement by Interaural Magnification." Acta oto-laryngologica vol. 109 (1990): 215-223. doi: https://doi.org/10.1080/00016489.1990.12088432
Kollmeier B, Peissig J. Speech Intelligibility Enhancement by Interaural Magnification. Acta Otolaryngol. 1990;109:215-223. doi: 10.1080/00016489.1990.12088432. PMID: 31905538.
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Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.

JMIR medical informatics

P Tafti A, Badger J, LaRose E, Shirzadi E, Mahnke A, Mayer J, Ye Z, Page D, Peissig P.
PMID: 29222076
JMIR Med Inform. 2017 Dec 08;5(4):e51. doi: 10.2196/medinform.9170.

BACKGROUND: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use...

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P Tafti A, Badger J, LaRose E, et al. Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Med Inform. 2017;5(4):e51doi: 10.2196/medinform.9170.
P Tafti, A., Badger, J., LaRose, E., Shirzadi, E., Mahnke, A., Mayer, J., Ye, Z., Page, D., & Peissig, P. (2017). Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR medical informatics, 5(4), e51. https://doi.org/10.2196/medinform.9170
P Tafti, Ahmad, et al. "Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure." JMIR medical informatics vol. 5,4 (2017): e51. doi: https://doi.org/10.2196/medinform.9170
P Tafti A, Badger J, LaRose E, Shirzadi E, Mahnke A, Mayer J, Ye Z, Page D, Peissig P. Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Med Inform. 2017 Dec 08;5(4):e51. doi: 10.2196/medinform.9170. PMID: 29222076; PMCID: PMC5741828.
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Quantifying predictive capability of electronic health records for the most harmful breast cancer.

Proceedings of SPIE--the International Society for Optical Engineering

Wu Y, Fan J, Peissig P, Berg R, Tafti AP, Yin J, Yuan M, Page D, Cox J, Burnside ES.
PMID: 29706685
Proc SPIE Int Soc Opt Eng. 2018 Feb;10577. doi: 10.1117/12.2293954. Epub 2018 Mar 07.

Improved prediction of the "most harmful" breast cancers that cause the most substantive morbidity and mortality would enable physicians to target more intense screening and preventive measures at those women who have the highest risk; however, such prediction models...

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Wu Y, Fan J, Peissig P, et al. Quantifying predictive capability of electronic health records for the most harmful breast cancer. Proc SPIE Int Soc Opt Eng. 2018;10577doi: 10.1117/12.2293954.
Wu, Y., Fan, J., Peissig, P., Berg, R., Tafti, A. P., Yin, J., Yuan, M., Page, D., Cox, J., & Burnside, E. S. (2018). Quantifying predictive capability of electronic health records for the most harmful breast cancer. Proceedings of SPIE--the International Society for Optical Engineering, 10577. https://doi.org/10.1117/12.2293954
Wu, Yirong, et al. "Quantifying predictive capability of electronic health records for the most harmful breast cancer." Proceedings of SPIE--the International Society for Optical Engineering vol. 10577 (2018). doi: https://doi.org/10.1117/12.2293954
Wu Y, Fan J, Peissig P, Berg R, Tafti AP, Yin J, Yuan M, Page D, Cox J, Burnside ES. Quantifying predictive capability of electronic health records for the most harmful breast cancer. Proc SPIE Int Soc Opt Eng. 2018 Feb;10577. doi: 10.1117/12.2293954. Epub 2018 Mar 07. PMID: 29706685; PMCID: PMC5914175.
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High-Dimensional Structured Feature Screening Using Binary Markov Random Fields.

JMLR workshop and conference proceedings

Liu J, Peissig P, Zhang C, Burnside E, McCarty C, Page D.
PMID: 23606924
JMLR Workshop Conf Proc. 2012;22:712-721.

Feature screening is a useful feature selection approach for high-dimensional data when the goal is to identify

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Liu J, Peissig P, Zhang C, et al. High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. JMLR Workshop Conf Proc. 2012;22:712-721
Liu, J., Peissig, P., Zhang, C., Burnside, E., McCarty, C., & Page, D. (2012). High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. JMLR workshop and conference proceedings, 22712-721.
Liu, Jie, et al. "High-Dimensional Structured Feature Screening Using Binary Markov Random Fields." JMLR workshop and conference proceedings vol. 22 (2012): 712-721.
Liu J, Peissig P, Zhang C, Burnside E, McCarty C, Page D. High-Dimensional Structured Feature Screening Using Binary Markov Random Fields. JMLR Workshop Conf Proc. 2012;22:712-721. PMID: 23606924; PMCID: PMC3630518.
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Showing 1 to 12 of 53 entries