Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system

Moon, Kyoung-Ja; Jin, Yinji; Jin, Taixian; Lee, Sun-Mi

doi:10.1016/j.ijnurstu.2017.09.014

Cited by 33 publications

(34 citation statements)

References 36 publications

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“…Given the availability of long-term surgical outcome data and advance machine learning methods, it is now possible to investigate the formulation of data-driven prediction models to pre-emptively identify patients susceptible to postsurgery delirium. LR-based prediction models to detect delirium have been developed using patient data from electronic medical records—in one study advanced text mining has been applied to abstract relevant data from clinical notes [82], and in another study attribute-based triggers were used [57]. We contend that with the availability of large volumes of patient data (before, during, and after the medical intervention), there are practical opportunities to develop data-driven prediction models to detect postoperative delirium in patients.…”

Section: Discussionmentioning

confidence: 99%

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Mufti¹,

Hirsch²,

Abidi³

et al. 2019

JMIR Med Inform

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BackgroundDelirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited.ObjectiveThis study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance.MethodsWe investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees.ResultsOnly 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03).ConclusionsAlthough delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

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Section: Discussionmentioning

confidence: 99%

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Mufti¹,

Hirsch²,

Abidi³

et al. 2019

JMIR Med Inform

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“…Indeed, machine learning approaches trained on English-language psychiatric notes have shown promising results with values of the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) of 0.85 and higher [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

Mosteiro¹,

Rijcken²,

Zervanou³

et al. 2021

JoAIMS

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Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique information, but are seldom used to their full potential. We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes. The performance of our best models is comparable to the currently used questionnaire-based method, with an area under the Receiver Operating Characteristic curve of approximately 0.8. We find that the deep-learning model BERTje performs worse than conventional machine learning methods. We also evaluate our data and our classifiers to understand the performance of our models better. This is particularly important for the applicability of evaluated classifiers to new data, and is also of great interest to practitioners, due to the increased availability of new data in electronic format.

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“…In this review, 14 studies reported model development (21)(22)(23)(24)(25)(27)(28)(29)(30)32,36,(39)(40)(41), and 19 studies reported model validation (21)(22)(23)(25)(26)(27)(28)(29)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41). The method of model development was logistic regression.…”

Section: Statistical Methods Of Modelsmentioning

confidence: 99%

Risk predictive models for delirium in the intensive care unit: a systematic review and meta-analysis

Chen¹,

Lao²,

Zhang³

et al. 2021

Ann Palliat Med

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Background: An emerging approach to prevent delirium in an intensive care unit is the use of risk prediction models. At present, there is no scientific comparison of the predictive effect of the prediction model. This systematic review and meta-analysis aimed to compare the performance of available delirium risk prediction models for intensive care units.Methods: As of June 1st, 2019, articles on delirium prediction models of the intensive care patients were

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Development and validation of an automated delirium risk assessment system (Auto-DelRAS) implemented in the electronic health record system

Cited by 33 publications

References 36 publications

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes

Risk predictive models for delirium in the intensive care unit: a systematic review and meta-analysis

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