Supreeth P. Shashikumar

Orcid: 0000-0002-0348-4261

Affiliations:
  • 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 17 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Bibliography

2024
Impact of a deep learning sepsis prediction model on quality of care and survival.
npj Digit. Medicine, 2024

2023
Unsupervised Detection and Correction of Model Calibration Shift at Test-Time.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.
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

Artificial intelligence sepsis prediction algorithm learns to say "I don't know".
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

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.
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

A FHIR-Enabled Streaming Sepsis Prediction System for ICUs.
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


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