Tezcan Ozrazgat Baslanti

Miguel Contreras, Brandon Silva, Benjamin Shickel et al.

The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable. Early detection of deteriorating conditions can result in providing timely interventions and improved survival rates. In this study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a 150k-parameter state space-based neural network to predict acuity state, transitions, and the need for life-sustaining therapies in real-time in ICU patients. The model uses data obtained in the prior four hours in the ICU and patient information obtained at admission to predict the acuity outcomes in the next four hours. We validated APRICOT-M externally on data from hospitals not used in development (75,668 patients from 147 hospitals), temporally on data from a period not used in development (12,927 patients from one hospital from 2018-2019), and prospectively on data collected in real-time (215 patients from one hospital from 2021-2023) using three large datasets: the University of Florida Health (UFH) dataset, the electronic ICU Collaborative Research Database (eICU), and the Medical Information Mart for Intensive Care (MIMIC)-IV. The area under the receiver operating characteristic curve (AUROC) of APRICOT-M for mortality (external 0.94-0.95, temporal 0.97-0.98, prospective 0.96-1.00) and acuity (external 0.95-0.95, temporal 0.97-0.97, prospective 0.96-0.96) shows comparable results to state-of-the-art models. Furthermore, APRICOT-M can predict transitions to instability (external 0.81-0.82, temporal 0.77-0.78, prospective 0.68-0.75) and need for life-sustaining therapies, including mechanical ventilation (external 0.82-0.83, temporal 0.87-0.88, prospective 0.67-0.76), and vasopressors (external 0.81-0.82, temporal 0.73-0.75, prospective 0.66-0.74). This tool allows for real-time acuity monitoring in critically ill patients and can help clinicians make timely interventions.

Subhash Nerella, Ziyuan Guan, Andrea Davidson et al.

Intensive Care Units (ICU) provide close supervision and continuous care to patients with life-threatening conditions. However, continuous patient assessment in the ICU is still limited due to time constraints and the workload on healthcare providers. Existing patient assessments in the ICU such as pain or mobility assessment are mostly sporadic and administered manually, thus introducing the potential for human errors. Developing Artificial intelligence (AI) tools that can augment human assessments in the ICU can be beneficial for providing more objective and granular monitoring capabilities. For example, capturing the variations in a patient's facial cues related to pain or agitation can help in adjusting pain-related medications or detecting agitation-inducing conditions such as delirium. Additionally, subtle changes in visual cues during or prior to adverse clinical events could potentially aid in continuous patient monitoring when combined with high-resolution physiological signals and Electronic Health Record (EHR) data. In this paper, we examined the association between visual cues and patient condition including acuity status, acute brain dysfunction, and pain. We leveraged our AU-ICU dataset with 107,064 frames collected in the ICU annotated with facial action units (AUs) labels by trained annotators. We developed a new "masked loss computation" technique that addresses the data imbalance problem by maximizing data resource utilization. We trained the model using our AU-ICU dataset in conjunction with three external datasets to detect 18 AUs. The SWIN Transformer model achieved 0.57 mean F1-score and 0.89 mean accuracy on the test set. Additionally, we performed AU inference on 634,054 frames to evaluate the association between facial AUs and clinically important patient conditions such as acuity status, acute brain dysfunction, and pain.

Brandon Silva, Miguel Contreras, Tezcan Ozrazgat Baslanti et al.

Acute brain dysfunctions (ABD), which include coma and delirium, are prevalent in the ICU, especially among older patients. The current approach in manual assessment of ABD by care providers may be sporadic and subjective. Hence, there exists a need for a data-driven robust system automating the assessment and prediction of ABD. In this work, we develop a machine learning system for real-time prediction of ADB using Electronic Health Record (HER) data. Our data processing pipeline enables integration of static and temporal data, and extraction of features relevant to ABD. We train several state-of-the-art transformer models and baseline machine learning models including CatBoost and XGB on the data that was collected from patients admitted to the ICU at UF Shands Hospital. We demonstrate the efficacy of our system for tasks related to acute brain dysfunction including binary classification of brain acuity and multi-class classification (i.e., coma, delirium, death, or normal), achieving a mean AUROC of 0.953 on our Long-former implementation. Our system can then be deployed for real-time prediction of ADB in ICUs to reduce the number of incidents caused by ABD. Moreover, the real-time system has the potential to reduce costs, duration of patients stays in the ICU, and mortality among those afflicted.

Scott Siegel, Jiaqing Zhang, Sabyasachi Bandyopadhyay et al.

Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captured in the ICU. Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all. In the past few years, the computer vision field has found application in many domains by reducing the human burden. Using computer vision systems in the ICU can also potentially enable non-existing assessments or enhance the frequency and accuracy of existing assessments while reducing the staff workload. In this study, we leverage a state-of-the-art noninvasive computer vision system based on depth imaging to characterize ICU visitations and patients' mobility. We then examine the relationship between visitation and several patient outcomes, such as pain, acuity, and delirium. We found an association between deteriorating patient acuity and the incidence of delirium with increased visitations. In contrast, self-reported pain, reported using the Defense and Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations. Our findings highlight the feasibility and potential of using noninvasive autonomous systems to monitor ICU patients.

Brandon Silva, Miguel Contreras, Sabyasachi Bandyopadhyay et al.

Assessing acute brain dysfunction (ABD), including delirium and coma in the intensive care unit (ICU), is a critical challenge due to its prevalence and severe implications for patient outcomes. Current diagnostic methods rely on infrequent clinical observations, which can only determine a patient's ABD status after onset. Our research attempts to solve these problems by harnessing Electronic Health Records (EHR) data to develop automated methods for ABD prediction for patients in the ICU. Existing models solely predict a single state (e.g., either delirium or coma), require at least 24 hours of observation data to make predictions, do not dynamically predict fluctuating ABD conditions during ICU stay (typically a one-time prediction), and use small sample size, proprietary single-hospital datasets. Our research fills these gaps in the existing literature by dynamically predicting delirium, coma, and mortality for 12-hour intervals throughout an ICU stay and validating on two public datasets. Our research also introduces the concept of dynamically predicting critical transitions from non-ABD to ABD and between different ABD states in real time, which could be clinically more informative for the hospital staff. We compared the predictive performance of two state-of-the-art neural network models, the MAMBA selective state space model and the Longformer Transformer model. Using the MAMBA model, we achieved a mean area under the receiving operator characteristic curve (AUROC) of 0.95 on outcome prediction of ABD for 12-hour intervals. The model achieves a mean AUROC of 0.79 when predicting transitions between ABD states. Our study uses a curated dataset from the University of Florida Health Shands Hospital for internal validation and two publicly available datasets, MIMIC-IV and eICU, for external validation, demonstrating robustness across ICU stays from 203 hospitals and 140,945 patients.

Zheng Feng, Rajendra Rana Bhat, Xiaoyong Yuan et al.

Surgery risk assessment is an effective tool for physicians to manage the treatment of patients, but most current research projects fall short in providing a comprehensive platform to evaluate the patients' surgery risk in terms of different complications. The recent evolution of big data analysis techniques makes it possible to develop a real-time platform to dynamically analyze the surgery risk from large-scale patients information. In this paper, we propose the Intelligent Perioperative System (IPS), a real-time system that assesses the risk of postoperative complications (PC) and dynamically interacts with physicians to improve the predictive results. In order to process large volume patients data in real-time, we design the system by integrating several big data computing and storage frameworks with the high through-output streaming data processing components. We also implement a system prototype along with the visualization results to show the feasibility of system design.