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Prasad, Niranjani
33  results:
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1

Deep learning for the prediction of clinical outcomes in in..:

Prasad, Niranjani ; Chien, Isabel ; Regan, Tim...
PLOS ONE.  18 (2023)  11 - p. e0272685 , 2023
Link: https://doi.org/10.1371/..
 
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2

Guiding Efficient, Effective, and Patient-Oriented Electrol..:

Prasad, Niranjani ; Mandyam, Aishwarya ; Chivers, Corey...
Journal of Personalized Medicine.  12 (2022)  5 - p. 661 , 2022
Link: https://doi.org/10.3390/..
 
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3

Defining admissible rewards for high-confidence policy eval..:

, In: Proceedings of the ACM Conference on Health, Inference, and Learning,
Prasad, Niranjani ; Engelhardt, Barbara ; Doshi-Velez, Finale - p. 1-9 , 2020
Link: https://dl.acm.org/doi/1..
 
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4

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoi..:

Pradier, Melanie F ; Prasad, Niranjani ; Chapfuwa, Paidamoyo...
http://arxiv.org/abs/2304.13737.  , 2023
Link: http://arxiv.org/abs/230..
 
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5

Guiding Efficient, Effective, and Patient-Oriented Electrol..:

Prasad, Niranjani ; Mandyam, Aishwarya ; Chivers, Corey...
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143326/.  , 2022
Link: http://www.ncbi.nlm.nih...
 
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6

An Optimal Policy for Patient Laboratory Tests in Intensive..:

Cheng, Li-Fang ; Prasad, Niranjani ; Engelhardt, Barbara E
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417830/.  , 2019
Link: http://www.ncbi.nlm.nih...
 
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7

Defining Admissible Rewards for High Confidence Policy Eval..:

Prasad, Niranjani ; Engelhardt, Barbara E ; Doshi-Velez, Finale
http://arxiv.org/abs/1905.13167.  , 2019
Link: http://arxiv.org/abs/190..
 
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8

An Optimal Policy for Patient Laboratory Tests in Intensive..:

Cheng, Li-Fang ; Prasad, Niranjani ; Engelhardt, Barbara E
http://arxiv.org/abs/1808.04679.  , 2018
Link: http://arxiv.org/abs/180..
 
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9

A Reinforcement Learning Approach to Weaning of Mechanical ..:

Prasad, Niranjani ; Cheng, Li-Fang ; Chivers, Corey..
http://arxiv.org/abs/1704.06300.  , 2017
Link: http://arxiv.org/abs/170..
 
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10

Scaling Recurrent Neural Network Language Models:

Williams, Will ; Prasad, Niranjani ; Mrva, David..
http://arxiv.org/abs/1502.00512.  , 2015
Link: http://arxiv.org/abs/150..
 
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11

RNN-Q test performances for PHQ-9 and GAD-7 at different ti..:

Niranjani Prasad ; Isabel Chien ; Tim Regan...
doi:10.1371/journal.pone.0272685.t005.  , 2023
Link: https://doi.org/10.1371/..
 
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12

RNN-Q model performance on test dataset by review period pl..:

Niranjani Prasad ; Isabel Chien ; Tim Regan...
doi:10.1371/journal.pone.0272685.g003.  , 2023
Link: https://doi.org/10.1371/..
 
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13

Outcomes of interest definitions:

Niranjani Prasad ; Isabel Chien ; Tim Regan...
doi:10.1371/journal.pone.0272685.t003.  , 2023
Link: https://doi.org/10.1371/..
 
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14

Model featurization in benchmarks vs RNN:

Niranjani Prasad ; Isabel Chien ; Tim Regan...
doi:10.1371/journal.pone.0272685.s002.  , 2023
Link: https://doi.org/10.1371/..
 
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15

External validation results:

Niranjani Prasad ; Isabel Chien ; Tim Regan...
doi:10.1371/journal.pone.0272685.s004.  , 2023
Link: https://doi.org/10.1371/..
 
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