Mohammad Adibuzzaman

TL;DR: This paper identifies and presents the initial work addressing the relevant challenges in three broad categories: data, accessibility, and translation in the context of a widely used detailed database from an intensive care unit, Medical Information Mart for Intensive Care (MIMIC III) database.

TL;DR: A novel methodology to classify electrocardiograms to normal, atrial fibrillation and other cardiac dysrhythmias as defined by the PhysioNet Challenge 2017, using piecewise linear splines for the feature selection and a gradient boosting algorithm for the classifier.

TL;DR: Though in its early deployment stages, e-ESAS demonstrates the potential to positively impact the cancer care by helping the doctors with graphical charts of long symptom history, facilitating timely interventions through alert generation and improving three way communications for a better decision making process and thereby improving the quality of life of BC patients.

TL;DR: A new model for collaborative access, exploration, and analyses of the Medical Information Mart for Intensive Care — III (MIMIC III) database for translational clinical research is described, which addresses problems of data integration, preprocessing, normalization, analyses, and visualization.

TL;DR: In this paper, the authors identify and present the initial work addressing the relevant challenges in three broad categories: data, accessibility, and translation in the context of a widely used detailed database from an intensive care unit.