Ritankar Das

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Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs

Yifan Zhou, Christopher W. Barton, Uli K. Chettipally, 2019, Comput. Biol. Medicine.

Research data supporting "Machine Learning Landscapes and Predictions for Patient Outcomes"

Ritankar Das, David J. Wales, 2017 .

Analyzing epileptogenic brain connectivity networks using clinical EEG data

Rajat K. De, Abhijit Dasgupta, Ritankar Das, 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data

Rajat K. De, Abhijit Dasgupta, Ritankar Das, 2017, PReMI.

Energy landscapes for machine learning.

Levent Sagun, Dhagash Mehta, Ritankar Das, 2017, Physical chemistry chemical physics : PCCP.

Multicenter validation of a machine learning algorithm for 48 hour all-cause mortality prediction

Philip B. Stark, Christopher Barton, Hamid Mohamadlou, 2018, bioRxiv.

A computational approach to mortality prediction of alcohol use disorder inpatients

Christopher Barton, Hamid Mohamadlou, Ritankar Das, 2016, Comput. Biol. Medicine.

A computational approach to early sepsis detection

Christopher W. Barton, Uli K. Chettipally, Jacob S. Calvert, 2016, Comput. Biol. Medicine.

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

Ritankar Das, Jonathan Roberts, Hoyt Burdick, 2020, BMC Medical Informatics and Decision Making.

Discharge recommendation based on a novel technique of homeostatic analysis

Christopher W. Barton, Uli K. Chettipally, Jacob S. Calvert, 2017, J. Am. Medical Informatics Assoc..

Two-Class in Silico Categorization of Intermediate Epileptic EEG Data

Rajat K. De, Abhijit Dasgupta, Ritankar Das, 2019, PReMI.

Machine learning landscapes and predictions for patient outcomes

David J Wales, Ritankar Das, D. Wales, 2017, Royal Society Open Science.

Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

Jacob S. Calvert, Ritankar Das, Anurag Garikipati, 2021, BioData Min..

Deep Learning with Diverse Objectives Improves ARDS Prediction

Ritankar Das, Carson K. Lam, Qingqing Mao, 2021 .

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

Ritankar Das, Qingqing Mao, Jacob Calvert, 2021, Applied Sciences.

Using electronic health record collected clinical variables to predict medical intensive care unit mortality

Hamid Mohamadlou, Uli K. Chettipally, Ritankar Das, 2016, Annals of medicine and surgery.

Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units

Ritankar Das, R. Das, Andrea McCoy, 2017, BMJ open quality.

Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach

Ari Ercole, David J Wales, Ritankar Das, 2017, BMJ Open.

Prediction of Acute Kidney Injury with a Machine Learning Algorithm using Electronic Health Record Data

Hamid Mohamadlou, Lisa Shieh, Anna Lynn-Palevsky, 2017 .

Cost and mortality impact of an algorithm-driven sepsis prediction system

Yaniv Kerem, Uli K. Chettipally, Ritankar Das, 2017, Journal of medical economics.

Machine learning prediction for classification of outcomes in local minimisation

Ritankar Das, David J. Wales, D. Wales, 2017 .

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

Sidney Le, Ritankar Das, Jacob Calvert, 2020, Journal of critical care.

Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial

Ritankar Das, C. Barton, David Shimabukuro, 2017, BMJ Open Respiratory Research.

Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting

Yaniv Kerem, Ritankar Das, Grant Fletcher, 2017, Biomedical informatics insights.

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

Ritankar Das, Joseph Slote, Eduardo Pino, 2018, BMJ Health & Care Informatics.

Energy landscapes for a machine-learning prediction of patient discharge.

David J Wales, Ritankar Das, D. Wales, 2016, Physical review. E.

High-performance detection and early prediction of septic shock for alcohol-use disorder patients

Hamid Mohamadlou, Uli K. Chettipally, Ritankar Das, 2016, Annals of medicine and surgery.