Supreeth P. Shashikumar
Orcid: 0000-0002-0348-4261Affiliations:
- University of California San Diego, La Jolla, CA, USA
- Georgia Institute of Technology, Atlanta, GA, USA (PhD 2020)
According to our database1,
Supreeth P. Shashikumar
authored at least 19 papers
between 2017 and 2024.
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Bibliography
2024
Author Correction: Impact of a deep learning sepsis prediction model on quality of care and survival.
npj Digit. Medicine, 2024
npj Digit. Medicine, 2024
CoRR, 2024
2023
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Predicting Hospital Readmission among Patients with Sepsis Using Clinical and Wearable Data.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
2022
Inclusion of social determinants of health improves sepsis readmission prediction models.
J. Am. Medical Informatics Assoc., 2022
A Comparison of Uncertainty Estimation Methods for Deep Learning-based Clinical Risk Scores.
Proceedings of the AMIA 2022, 2022
2021
Generalizable Models for Prediction of Physiological Decompensation from multivariate and Multiscale Physiological Time Series using Deep Learning and Transfer Learning Techniques.
PhD thesis, 2021
npj Digit. Medicine, 2021
DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis.
Artif. Intell. Medicine, 2021
2020
AIDEx - An Open-source Platform for Real-Time Forecasting Sepsis and A Case Study on Taking ML Algorithms to Production.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
Proceedings of the AMIA 2020, 2020
2019
DeepAISE - An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis.
CoRR, 2019
Does the "Artificial Intelligence Clinician" learn optimal treatment strategies for sepsis in intensive care?
CoRR, 2019
Early Prediction of Sepsis from Clinical Data: the PhysioNet/Computing in Cardiology Challenge 2019.
Proceedings of the 46th Computing in Cardiology, 2019
2018
Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018
2017
A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology.
Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics, 2017