Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study

Balkan, Baran; Essay, Patrick; Subbian, Vignesh

doi:10.1109/embc.2018.8513324

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…Although the predictions for the survivors are reasonably accurate, the predictions for nonsurvivors are not. We include this to illustrate that although predictive models are useful in certain situations, they may not perform well in others because of the dynamics involved or issues with source data [14]. These results are consistent with evaluations of earlier versions of APACHE predictions [15] and are an area of improvement for tele-ICU to provide the best possible decision support for the fast-paced ICU environment.…”

Section: Resultssupporting

confidence: 62%

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

Essay¹,

Shahin²,

Balkan³

et al. 2019

JMIR Med Inform

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Background Many intensive care units (ICUs) utilize telemedicine in response to an expanding critical care patient population, off-hours coverage, and intensivist shortages, particularly in rural facilities. Advances in digital health technologies, among other reasons, have led to the integration of active, well-networked critical care telemedicine (tele-ICU) systems across the United States, which in turn, provide the ability to generate large-scale remote monitoring data from critically ill patients. Objective The objective of this study was to explore opportunities and challenges of utilizing multisite, multimodal data acquired through critical care telemedicine. Using a publicly available tele-ICU, or electronic ICU (eICU), database, we illustrated the quality and potential uses of remote monitoring data, including cohort discovery for secondary research. Methods Exploratory analyses were performed on the eICU Collaborative Research Database that includes deidentified clinical data collected from adult patients admitted to ICUs between 2014 and 2015. Patient and ICU characteristics, top admission diagnoses, and predictions from clinical scoring systems were extracted and analyzed. Additionally, a case study on respiratory failure patients was conducted to demonstrate research prospects using tele-ICU data. Results The eICU database spans more than 200 hospitals and over 139,000 ICU patients across the United States with wide-ranging clinical data and diagnoses. Although mixed medical-surgical ICU was the most common critical care setting, patients with cardiovascular conditions accounted for more than 20% of ICU stays, and those with neurological or respiratory illness accounted for nearly 15% of ICU unit stays. The case study on respiratory failure patients showed that cohort discovery using the eICU database can be highly specific, albeit potentially limiting in terms of data provenance and sparsity for certain types of clinical questions. Conclusions Large-scale remote monitoring data sources, such as the eICU database, have a strong potential to advance the role of critical care telemedicine by serving as a testbed for secondary research as well as for developing and testing tools, including predictive and prescriptive analytical solutions and decision support systems. The resulting tools will also inform coordination of care for critically ill patients, intensivist coverage, and the overall process of critical care telemedicine.

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Section: Resultssupporting

confidence: 62%

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

Essay¹,

Shahin²,

Balkan³

et al. 2019

JMIR Med Inform

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the RMSPE and MAE obtained by the proposed model have significant promising results. RMSPE is about 4 days lower than the study of Verburg et al [13], and MAE is about 2 days and 1 days lower than the studies of Verburg et al [13] and Balkan and Subbian [39], respectively. It shows that the proposed model have substantial advantages over other LoS prediction models.…”

Section: B Comparison Of Different Regression Methodscontrasting

confidence: 65%

Prediction of Length of Stay on the Intensive Care Unit Based on Least Absolute Shrinkage and Selection Operator

Chen

Feng

et al. 2019

IEEE Access

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Length of stay (LoS) in the intensive care unit (ICU) is a common outcome measure used as an indicator of both quality of care and resource use. However, the existing analysis methods of LoS are poorly interpretable and extensible, and there is controversial for the predictive performance of LoS. In this paper, the study includes data from 1,214 unplanned ICU admissions to participate in the ICU of Sichuan Provincial People's Hospital between Dec. 11, 2015 and Dec. 6, 2018. On the basis of these data, this study creates a highly accurate and predictive model using advanced preprocessing techniques, exploratory data analysis (EDA) and least absolute shrinkage and selection operator (LASSO) algorithm. Next, this study evaluates the predictive performance of the proposed model by 10-fold cross validation and external validation method using the root mean square prediction error (RMSPE), mean absolute error (MAE), and coefficient of determination (R 2). The predictive performance of the proposed model is 0.88±0.13 day for RMSPE, 0.87±0.07 day for MAE and 0.35±0.09 for R 2. Experimental results show that the performance of the proposed method are competitive with the state-of-the-art methods and results. Furthermore, this study explores the risk factors for ICU LoS in survivors and non-survivors and compare their predictive performance. INDEX TERMS Length of stay, intensive care unit, exploratory data analysis, least absolute shrinkage and selection operator.

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“…Intensive monitoring through the ICU equipment results in large medical records that require efficient and accurate systems for assistance in data analysis. Using ICU data to predict future events, such as patient mortality, is considered one of the most critical topics in ICU research [40]. In this section, we discuss the related literature studies on this topic.…”

Section: Related Workmentioning

confidence: 99%

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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The intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great risk of dying and for providing suitable interventions to save their lives. Accordingly, early prediction of patients at high mortality risk will enable their provision of appropriate and timely medical services. Although various severity scores and machine-learning models have recently been developed for early mortality prediction, such prediction remains challenging. This paper proposes a novel stacking ensemble approach to predict the mortality of ICU patients. Our approach is more accurate and medically intuitive compared to the literature work. Data were prepared and feature selection was processed under the supervision of the ICU domain expert. The data were split into six modalities based on the expert's decisions. For the prediction process, a separate classifier was selected for each modality based on the performance of the classifiers. We utilized the most popular and diverse classifiers in the literature, including linear discriminant analysis, decision tree (DT), multilayer perceptron, k-nearest neighbor, and logistic regression (LR). Then, a stacking ensemble classifier was constructed and optimized based on the fusion of these five classifier decisions. The framework was evaluated using 10,664 patients from the medical information mart for intensive care (MIMIC III) benchmark dataset. To predict patient mortality, extensive experiments were conducted using the patients' time series data of different lengths. For each patient, the first 6, 12, and 24 hours of the first stay were tested. The results indicate that our model outperformed the state-of-the-art approaches in terms of accuracy (94.4%), F1 score (93.7%), precision (96.4%), recall (91.1%), and area under the receiver operator characteristic (ROC) curve (93.3%). These results demonstrate the ability and efficiency of our approach to predict ICU mortality.

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Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study

Cited by 21 publications

References 9 publications

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

The Connected Intensive Care Unit Patient: Exploratory Analyses and Cohort Discovery From a Critical Care Telemedicine Database

Prediction of Length of Stay on the Intensive Care Unit Based on Least Absolute Shrinkage and Selection Operator

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

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