Intelligent Intensive Care Unit

In the intensive care unit (ICU), time is precious in terms of patient condition, clinician attention, and money. Annually in the United States, 5.7 million patients are admitted to ICUs, with costs exceeding $82 billion.

Healthcare providers must monitor and assess patient acuity as a part of ICU care, but their assessments may be sporadic due to overburdened clinicians and only reflect static, imprecise moments of a patient’s condition due to manual calculations. As a result, there is a critical unmet need for real-time interpretable, dynamic, autonomous, and precise acuity prediction in the ICU that integrates visual assessment of the patient with continuous physiologic measurement and clinical data.

Therefore, the Intelligent ICU project (I2CU) aims to develop and validate real-time autonomous acuity assessment and prediction tools using novel deep learning (DL) and sensing technologies. Our hypothesis is that through integrating clinical and physiological data with real-time autonomous assessment of visual cues (e.g., pain, emotional distress, physical function), DL models will outperform existing clinical models for predicting patient acuity.

To create this tool, researchers are pursuing the following goals.

For Interpretable Acuity Prediction

Develop and validate an interpretable DL model to determine if it the DL model more accurate than current models in predicting transitions in the acuity of a patient’s daily clinical status, while providing interpretable information to physicians. Integrated electronic health record and continuous physiological data will help train and validate the DL model.

I2CU graphic of the acuity prediction model's architecture and mock visualized output.
Acuity prediction model data flow and expected output.

For Autonomous Visual Assessment

Develop a pervasive sensing system that autonomously captures 33 visual cues of critically ill patients and determine if it can provide accurate visual assessments compared to human experts.

For Real-Time Intelligence

Implement and evaluate an intelligent platform for real-time integration of autonomous visual assessment and acuity prediction in clinical workflows to determine accuracy in real-time prospective evaluation and to determine physicians’ satisfaction with the risk assessment process.

A simulated ICU room graphic with components of the I2CU model labeled around the room.
A conceptualization of the I2CU system in an ICU room.

Successful application of the proposed technology would augment clinical decision-making in the fast-paced environment of ICU to promote earlier and more targeted interventions. Real-time, precise predictions of clinical trajectory reduce the need for manual, repetitive ICU assessments and capture patient aspects that were previously unnoticed. Ultimately, the results are expected to improve patient outcomes, decrease hospitalization costs, and reduce lifelong complications.

Select I2CU Publications

News Segments about I2CU

More I2CU Information