S
Sakyajit Bhattacharya
Researcher at Xerox
Publications - 38
Citations - 301
Sakyajit Bhattacharya is an academic researcher from Xerox. The author has contributed to research in topics: Intensive care & Computer science. The author has an hindex of 8, co-authored 35 publications receiving 198 citations. Previous affiliations of Sakyajit Bhattacharya include Tata Consultancy Services.
Papers
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Proceedings Article
ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets.
TL;DR: A new algorithm for ICU mortality prediction is presented that is designed to address the problem of imbalance, which occurs, in the context of binary classification, when one of the two classes is significantly under–represented in the data.
Journal ArticleDOI
Predicting Complications in Critical Care Using Heterogeneous Clinical Data
Vijay Huddar,Bapu Koundinya Desiraju,Vaibhav Rajan,Sakyajit Bhattacharya,Shourya Roy,Chandan K. Reddy +5 more
TL;DR: A new preprocessing technique for extracting features from informal clinical notes that can be used in a classification model to identify patients at risk of developing complications is presented and the use of collective matrix factorization, a multi-view learning technique, is explored to model heterogeneous clinical data-text-based features in combination with other measurements.
Proceedings ArticleDOI
Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system
TL;DR: The aim of this paper is to generate synthetic PPG signal from a Digital twin platform replicating cardiovascular system to simulate specific ‘what if’ scenarios as well as to generate large scale synthetic data with patho-physiological interpretability.
Proceedings ArticleDOI
Automated lung sound analysis for detecting pulmonary abnormalities
TL;DR: Novel spectral and spectrogram features are introduced, which are further refined by Maximal Information Coefficient, leading to the classification of healthy and abnormal lung sounds.
Proceedings Article
Dependency clustering of mixed data with Gaussian mixture copulas
TL;DR: A new, efficient, semiparametric algorithm is designed to approximately estimate the parameters of the copula that can fit continuous, ordinal and binary data and empirically demonstrate performance improvements over state-of-the-art methods of correlation clustering on synthetic and benchmark datasets.