naimspace
Research Scholar, NIT GOA. Critical Care Informatics, Physiological Signal Processing
National Institute of Technology Goa, IndiaGoa, India
Pinned Repositories
A-correlation-matrix-based-tensor-decomposition-method-for-early-prediction-of-sepsis
This repository is based on the code to implement our work titled “A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data” , Biocybernetics and Biomedical Engineering, vol.41, no.3, pp.1013– 1024, 2021.
Early-Prediction-of-Sepsis-From-Clinical-Data-Using-Ratio-and-Power-Based-Features
This repository is based on the code to implement our work titled “Early-Prediction-of-Sepsis-From-Clinical-Data-Using-Ratio-and-Power-Based-Features” , Critical Care Medicine, vol.43, no.12, pp.e1343– e1349, 2021.
naimspace
Config files for my GitHub profile.
PhysioNet-2018-Challenge-entry-for-Sleep-arousal-classification-using-Physiological-Signals
This repository is based on the code to implement our work titled "Application-of-RNN-for-the-Prediction-of-Target-Non-Apneic-Arousal-Regions-in-Physiological-Signals", In Proceedings of 45th IEEE International Conference on Computing in Cardiology (CinC) (Vol. 45, pp. 1–4). IEEE, Maastricht, Netherlands. doi:https://doi.org/10.22489/CinC.2018.256
Tensor-Learning-of-PMI-for-Sepsis-Prediction
This repository is based on the code to implement our work titled “Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis” , Computers in Biology and Medicine, vol. 134, pp.104430, 2021.
naimspace's Repositories
naimspace/A-correlation-matrix-based-tensor-decomposition-method-for-early-prediction-of-sepsis
This repository is based on the code to implement our work titled “A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data” , Biocybernetics and Biomedical Engineering, vol.41, no.3, pp.1013– 1024, 2021.
naimspace/Early-Prediction-of-Sepsis-From-Clinical-Data-Using-Ratio-and-Power-Based-Features
This repository is based on the code to implement our work titled “Early-Prediction-of-Sepsis-From-Clinical-Data-Using-Ratio-and-Power-Based-Features” , Critical Care Medicine, vol.43, no.12, pp.e1343– e1349, 2021.
naimspace/naimspace
Config files for my GitHub profile.
naimspace/PhysioNet-2018-Challenge-entry-for-Sleep-arousal-classification-using-Physiological-Signals
This repository is based on the code to implement our work titled "Application-of-RNN-for-the-Prediction-of-Target-Non-Apneic-Arousal-Regions-in-Physiological-Signals", In Proceedings of 45th IEEE International Conference on Computing in Cardiology (CinC) (Vol. 45, pp. 1–4). IEEE, Maastricht, Netherlands. doi:https://doi.org/10.22489/CinC.2018.256
naimspace/Tensor-Learning-of-PMI-for-Sepsis-Prediction
This repository is based on the code to implement our work titled “Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis” , Computers in Biology and Medicine, vol. 134, pp.104430, 2021.