For a critically ill patient, an accurate predictive tool for biochemical markers based on the patient prior clinical data can aide physicians to design better patient-specific treatment plans. In this study, we develop a since dynamical system model based on neural networks capable of predicting concentrations of biochemical markers, including albumin, of a critically ill patient, in real-time.
The metabolic process of a patient follows a patient-specific dynamical system which can be uncovered with certain accuracy from sufficient prior data taken from the patient. For a given set of patient’s biochemical markers, the dynamical system represented by deep neural networks is discovered from the prior data via deep learning methods.
One critically ill, poly-trauma patient (injury severity score = 34 points) was enrolled in the study. Six biochemical markers (albumin (ALB), creatinine (Cr), osmotic pressure (OSM), alanine aminotransferase (ALT), total bilirubin (TB), direct bilirubin (DB)) were collected and exogenous albumin injection was administered to the patient for the total of 27 consecutive days during the study. A sliding window of data in 10 consecutive days was used as the training set and the 11th day's data as the test set to train and test the parameters in the neural network. The obtained dynamical system model is then used to forecast the chemical markers in the next 24 hours. The results are compared with the true clinical data with a relative error consistently less than 2%.
This study demonstrates that a dynamic system model can be established to monitor and predict concentrations of biochemical markers, including albumin, via neural networks and deep learning methods. This data-driven patient-specific modeling approach is applicable to any patient.
Metabolomics Dynamics Study for Severe Patient, Registered:17June,2014, https://www.clinicaltrials.gov/ct2/show/NCT02164786?term=NCT02164786&draw=2&rank=1