Fluid therapy is extensively used to treat traumatized patients as well as patients during surgery. The fluid therapy process is complex due to interpatient variability in response to therapy as well as other complicating factors such as comorbidities and general anesthesia. These complexities can result in under- or over-resuscitation. Given the complexity of the fluid management process as well as the increased capabilities in hemodynamic monitoring, closed-loop fluid management can reduce the workload of the overworked clinician while ensuring specific constraints on hemodynamic endpoints are met with higher accuracy. The goal of this paper is to provide an overview of closed-loop control systems for fluid management and highlight several key steps in transitioning such a technology from bench to the bedside.

Observer‐dependent infant pain scales have limitations including discontinuous assessments and the lack of healthcare professionals' availability. We hypothesized that applying agnostic machine learning approaches to neonatal electroencephalographic (EEG) analysis may reveal features of the infant response to acute pain. EEG was recorded from 30 neonates undergoing acutely painful procedures (18 males, 34.0–41.7 weeks gestation at birth). EEG recordings were randomly assigned to training (n = 20) and testing (n = 10) datasets. Functional connectivity measures were calculated for each infant before and after pain‐inducing procedures. A grid search including five machine learning models was conducted on the training dataset, and each model was evaluated using leave‐one‐subject‐out cross‐validation. An optimal model, having the highest F‐1 score, was obtained and evaluated on the independent testing dataset. A gradient boosting model with 12 features showed optimal performance, with 90% area under the receiver operating characteristic curve suggesting high specificity (0.90) and precision (0.90). The five highest ranked features corresponded to EEG electrode pairs: T7‐P4, Fz‐CP5, FC1‐TP10, CP6‐Cz, and Fz‐F3, suggesting involvement of the contralateral temporal gyrus, opercular cortex, thalamus, and bilateral insula in infant pain processing. Preliminary changes in functional connectivity indicate infant pain processing. Future machine learning algorithms can integrate physiological and behavioral parameters with EEG changes to accurately assess the complexity of infant pain responses.Trial Registration: ClinicalTrials.gov identifier: NCT03330496

This paper introduces a novel framework for fast parameter identification of personalized pharmacokinetic problems. Given one sample observation of a new subject, the framework predicts the parameters of the subject based on prior knowledge from a pharmacokinetic database. The feasibility of this framework was demonstrated by developing a new algorithm based on the Cluster Newton method, namely the constrained Cluster Newton method, where the initial points of the parameters are constrained by the database. The algorithm was tested with the compartmental model of propofol on a database of 59 subjects. The average overall absolute percentage error based on constrained Cluster Newton method is 12.10% with the threshold approach, and 13.42% with the nearest-neighbor approach. The average computation time of one estimation is 13.10 seconds. Using parallel computing, the average computation time is reduced to 1.54 seconds, achieved with 12 parallel workers. The results suggest that the proposed framework can effectively improve the prediction accuracy of the pharmacokinetic parameters with limited observations in comparison to the conventional methods. Computation cost analyses indicate that the proposed framework can take advantage of parallel computing and provide solutions within practical response times, leading to fast and accurate parameter identification of pharmacokinetic problems.