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13 results on '"Chandross, Karen J"'

2. Accelerating diagnosis of Parkinson’s disease through risk prediction

Author

Yuan, William, Beaulieu-Jones, Brett, Krolewski, Richard, Palmer, Nathan, Veyrat-Follet, Christine, Frau, Francesca, Cohen, Caroline, Bozzi, Sylvie, Cogswell, Meaghan, Kumar, Dinesh, Coulouvrat, Catherine, Leroy, Bruno, Fischer, Tanya Z., Sardi, S. Pablo, Chandross, Karen J., Rubin, Lee L., Wills, Anne-Marie, Kohane, Isaac, and Lipnick, Scott L.

Published
2021
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5. Technology-Enabled Clinical Trials: Transforming Medical Evidence Generation

Author

Marquis-Gravel, Guillaume, Roe, Matthew T., Turakhia, Mintu P., Boden, William, Temple, Robert, Sharma, Abhinav, Hirshberg, Boaz, Slater, Paul, Craft, Noah, Stockbridge, Norman, McDowell, Bryan, Waldstreicher, Joanne, Bourla, Ariel, Bansilal, Sameer, Wong, Jennifer L., Meunier, Claire, Kassahun, Helina, Coran, Philip, Bataille, Lauren, Patrick-Lake, Bray, Hirsch, Brad, Reites, John, Mehta, Rajesh, Muse, Evan D., Chandross, Karen J., Silverstein, Jonathan C., Silcox, Christina, Overhage, J. Marc, Califf, Robert M., and Peterson, Eric D.

Published
2019
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8. Additional file 1 of Accelerating diagnosis of Parkinson’s disease through risk prediction

Author

Yuan, William, Beaulieu-Jones, Brett, Krolewski, Richard, Palmer, Nathan, Veyrat-Follet, Christine, Frau, Francesca, Cohen, Caroline, Bozzi, Sylvie, Cogswell, Meaghan, Kumar, Dinesh, Coulouvrat, Catherine, Leroy, Bruno, Fischer, Tanya Z., S. Pablo Sardi, Chandross, Karen J., Rubin, Lee L., Wills, Anne-Marie, Kohane, Isaac, and Lipnick, Scott L.

Abstract

Additional file 1: Supplementary Methods. Table S1. Included and excluded codes for initial analysis. Table S2. Included and excluded codes for feature tracking and gait/tremor indexed analysis. Table S3. Tremor-only Cohort, PD associated Diagnoses. Table S4: Gait-only Cohort, PD associated Diagnoses.

Published
2021
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9. Calibration and Uncertainty in Neural Time-to-Event Modeling

Author

Chapfuwa, Paidamoyo, Tao, Chenyang, Li, Chunyuan, Khan, Irfan, Chandross, Karen J., Pencina, Michael J., Carin, Lawrence, and Henao, Ricardo

Abstract

Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times (i.e., relative risk). We propose neural time-to-event models that account for calibration and uncertainty while predicting accurate absolute event times. Specifically, an adversarial nonparametric model is introduced for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We also consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models. Extensive experiments show that the distribution matching methods outperform existing approaches in terms of both calibration and concentration of time-to-event distributions.

Published
2023
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12. In vitro myelin formation using embryonic stem cells

Author

Kerman, Bilal E., primary, Kim, Hyung Joon, additional, Padmanabhan, Krishnan, additional, Mei, Arianna, additional, Georges, Shereen, additional, Joens, Matthew S., additional, Fitzpatrick, James A. J., additional, Jappelli, Roberto, additional, Chandross, Karen J., additional, August, Paul, additional, and Gage, Fred H., additional

Published
2015
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