Predicting Individual Physiologically Acceptable States at Discharge from a Pediatric Intensive Care Unit
We aim to quantify acceptable ICU-discharge vitals, compare to age-normal vitals, and develop machine learning models to predict throughout each patient’s ICU episode. Our dataset of 7,256 surviving PICU episodes (5,632 patients) collected between 2009 and 2016 at Children's Hospital Los Angeles contains 375 variables representing vitals, labs, interventions, drugs, and medical and physical discharge times. The means of each patient's heart rate, systolic blood pressure, and diastolic blood pressure between medical and physical discharge were computed as their physiologically acceptable state space (PASS), which were compared to age-normal, regression, and recurrent neural network (RNN) predictions.
Mr. David Ledbetter has an extensive and deep understanding of decision theory. He has experience implementing various decision engines, including convolutional neural networks, random forests, extra trees, and linear discrimination analysis. His particular area of focus is in performance estimation, where he has demonstrated a tremendous ability to accurately predict performance on new data in nonstationary, real-world scenarios. David has worked on a number of real-world detection projects, including detecting circulating tumor cells in blood, automatic target recognition utilizing CNNs from satellite imagery, make/model car classification for the Los Angeles Police Department using CNNs, and acoustic right whale call detection from underwater sonobuoys. Recently, David has been developing an RNN to generate personalized treatment recommendations to optimize patient outcomes using electronic medical records from 15 years of data collected from the Children's Hospital Los Angeles Pediatric Intensive Care Unit.