Artificial intelligence and computer simulation models in critical illness

Table 1 Differences between associative artificial intelligence and actionable artificial intelligence models

Models based on associative artificial intelligence	Models based on actionable artificial intelligence
These applications are built using available historical public or institutional data repositories[26,31,32].	These applications are built more often on the prospectively collected data points, predicting risk vs benefit of a particular treatment or intervention[17,30,33,34].
Almost always based on retrospective data[35,36].	Developed using the data points that are collected prospectively in real-time[30,34].
Purely data driven associative models often without explicit consideration of causal pathways[37-39].	These models are developed with an understanding based on the underlying causal pathways, therefore providing greater clinical utility and accuracy[40-42].
Representative examples: Development and validation of a data driven tool to predict sepsis based on vital signs by Mao et al[43]. Provides no actionable benefit to the bedside clinician. Similarly, a model developed to predict AKI in a patient based on retrospectively collected dataset from electronic health records by Tomasev et al[26]. The model was associated with high false positive alerts (2 false positive alerts for each true alert).	Representative examples: Improving the safety of ventilator care by avoiding ventilator-induced lung injury. Electronic algorithm based on near real-time data and notification of bedside providers giving actionable information, developed by Herasevich et al[33]. Artificial neural network based model developed for forecasting ICP for medical decision support, by Zhang et al[42]. This model provided actionable treatment planning for patients based on the predicted future trends of ICP.

AKI: Acute kidney injury; ICP: Intracranial pressure.