Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics

no code implementations • 11 May 2018 • Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Paul Thottakkara, Ashkan Ebadi, Amir Motaei, Parisa Rashidi, Xiaolin Li, Azra Bihorac

We used machine learning and statistical analysis techniques to develop perioperative models to predict the risk of AKI (risk during the first 3 days, 7 days, and until the discharge day) before and after the surgery.

Time Series Time Series Analysis

DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

no code implementations • 28 Feb 2018 • Benjamin Shickel, Tyler J. Loftus, Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Azra Bihorac, Parisa Rashidi

Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds.

Decision Making