Predicting the Onset of Delirium on Hourly Basis in an Intensive Care Unit Following Cardiac Surgery

Lapp, Linda; Roper, Marc; Kavanagh, Kimberley; Schraag, Stefan

doi:10.1109/cbms55023.2022.00048

Cited by 2 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A further distinguishing feature of RMS is the five-minute time resolution at which predictions are made, enabling longitudinal analysis of risk trajectories. This dynamic prediction paradigm is more flexible than traditional severity scores, which are evaluated at fixed time-points, such as at 24 h after ICU admission 50 , mainly to predict ICU mortality 51 .…”

Section: Discussionmentioning

confidence: 99%

RMS: A ML-based system for ICU Respiratory Monitoring and Resource Planning

Hüser,

Lyu,

Faltys

et al. 2024

Preprint

View full text Add to dashboard Cite

Respiratory failure (RF) is a frequent occurrence in critically ill patients and is associated with significant morbidity and mortality as well as resource use. To improve the monitoring and management of RF in intensive care unit (ICU) patients, we used machine learning to develop a monitoring system covering the entire management cycle of RF, from early detection and monitoring, to assessment of readiness for extubation and prediction of extubation failure risk. For patients in the ICU in the study cohort, the system predicts 80% of RF events at a precision of 45% with 65% identified 10h before the onset of an RF event. This significantly improves upon a standard clinical baseline based on the SpO2/FiO2 ratio. After a careful analysis of ICU differences, the RF alarm system was externally validated showing similar performance for patients in the external validation cohort. Our system also provides a risk score for extubation failure for patients who are clinically ready to extubate, and we illustrate how such a risk score could be used to extubate patients earlier in certain scenarios. Moreover, we demonstrate that our system, which closely monitors respiratory failure, ventilation need, and extubation readiness for individual patients can also be used for ICU-level ventilator resource planning. In particular, we predict ventilator use 8-16h into the future, corresponding to the next ICU shift, with a mean absolute error of 0.4 ventilators per 10 patients effective ICU capacity.

show abstract

Section: Discussionmentioning

confidence: 99%

RMS: A ML-based system for ICU Respiratory Monitoring and Resource Planning

Hüser,

Lyu,

Faltys

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

Mehrabani-Zeinabad

Feizi

Sadeghi

et al. 2023

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Cardiovascular diseases (CVD) are the predominant cause of early death worldwide. Identification of people with a high risk of being affected by CVD is consequential in CVD prevention. This study adopts Machine Learning (ML) and statistical techniques to develop classification models for predicting the future occurrence of CVD events in a large sample of Iranians. Methods We used multiple prediction models and ML techniques with different abilities to analyze the large dataset of 5432 healthy people at the beginning of entrance into the Isfahan Cohort Study (ICS) (1990–2017). Bayesian additive regression trees enhanced with “missingness incorporated in attributes” (BARTm) was run on the dataset with 515 variables (336 variables without and the remaining with up to 90% missing values). In the other used classification algorithms, variables with more than 10% missing values were excluded, and MissForest imputes the missing values of the remaining 49 variables. We used Recursive Feature Elimination (RFE) to select the most contributing variables. Random oversampling technique, recommended cut-point by precision-recall curve, and relevant evaluation metrics were used for handling unbalancing in the binary response variable. Results This study revealed that age, systolic blood pressure, fasting blood sugar, two-hour postprandial glucose, diabetes mellitus, history of heart disease, history of high blood pressure, and history of diabetes are the most contributing factors for predicting CVD incidence in the future. The main differences between the results of classification algorithms are due to the trade-off between sensitivity and specificity. Quadratic Discriminant Analysis (QDA) algorithm presents the highest accuracy (75.50 ± 0.08) but the minimum sensitivity (49.84 ± 0.25); In contrast, decision trees provide the lowest accuracy (51.95 ± 0.69) but the top sensitivity (82.52 ± 1.22). BARTm.90% resulted in 69.48 ± 0.28 accuracy and 54.00 ± 1.66 sensitivity without any preprocessing step. Conclusions This study confirmed that building a prediction model for CVD in each region is valuable for screening and primary prevention strategies in that specific region. Also, results showed that using conventional statistical models alongside ML algorithms makes it possible to take advantage of both techniques. Generally, QDA can accurately predict the future occurrence of CVD events with a fast (inference speed) and stable (confidence values) procedure. The combined ML and statistical algorithm of BARTm provide a flexible approach without any need for technical knowledge about assumptions and preprocessing steps of the prediction procedure.

show abstract

Predicting the Onset of Delirium on Hourly Basis in an Intensive Care Unit Following Cardiac Surgery

Cited by 2 publications

References 25 publications

RMS: A ML-based system for ICU Respiratory Monitoring and Resource Planning

RMS: A ML-based system for ICU Respiratory Monitoring and Resource Planning

Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region

Contact Info

Product

Resources

About