12 citations · 28 across the 10 of their papers we have counts for
10 papers
Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning
Esra Adiyeke, Tianqi Liu, Venkata Sai Dheeraj Naganaboina +10
Traditional methods of surgical decision making heavily rely on human experience and prompt actions, which are variable. A data-driven system generating treatment recommendations b…
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
Esra Adiyeke, Yuanfang Ren, Benjamin Shickel +12
Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney…
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
Yuanfang Ren, Yanjun Li, Tyler J. Loftus +9
Initial hours of hospital admission impact clinical trajectory, but early clinical decisions often suffer due to data paucity. With clustering analysis for vital signs within six h…
Clinical Courses of Acute Kidney Injury in Hospitalized Patients: A Multistate Analysis
Esra Adiyeke, Yuanfang Ren, Ziyuan Guan +4
Objectives: We aim to quantify longitudinal acute kidney injury (AKI) trajectories and to describe transitions through progressing and recovery states and outcomes among hospitaliz…
Application of Deep Interpolation Network for Clustering of Physiologic Time Series
Yanjun Li, Yuanfang Ren, Tyler J. Loftus +7
Background: During the early stages of hospital admission, clinicians must use limited information to make diagnostic and treatment decisions as patient acuity evolves. However, it…
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks
Benjamin Shickel, Tyler J. Loftus, Matthew Ruppert +4
Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized tha…
Added Value of Intraoperative Data for Predicting Postoperative Complications: Development and Validation of a MySurgeryRisk Extension
Shounak Datta, Tyler J. Loftus, Matthew M. Ruppert +12
To test the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications improve when using both preoperative and intraoperative data input fea…
The DREAMS Project: Improving the Intensive Care Patient Experience with Virtual Reality
Triton Ong, Matthew Ruppert, Parisa Rashidi +3
Purpose: Preliminarily evaluate the feasibility and efficacy of using meditative virtual reality (VR) to improve the hospital experience of intensive care unit (ICU) patients. Meth…
Development and validation of computable Phenotype to Identify and Characterize Kidney Health in Adult Hospitalized Patients
Tezcan Ozrazgat-Baslanti, Amir Motaei, Rubab Islam +7
Background: Acute kidney injury (AKI) is a common complication in hospitalized patients and a common cause for chronic kidney disease (CKD) and increased hospital cost and mortalit…
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring
Anis Davoudi, Kumar Rohit Malhotra, Benjamin Shickel +8
Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In add…