Towards an automated multimodal clinical decision support system at the post anesthesia care unit

Olsen, Rasmus Munch; Aasvang, Eske Kvanner; Meyhoff, Christian S.; Sørensen, Helge Bjarup Dissing

doi:10.1016/j.compbiomed.2018.07.018

Cited by 24 publications

(12 citation statements)

References 13 publications

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“…The machine learning approach has been developed recently for advantages in performance and extensibility and has become indispensable for solving complex problems in most sciences [ 80 , 81 , 82 ]. This method is used to examine postoperative outcomes [ 83 , 84 , 85 , 86 ] and predict hypotension [ 87 , 88 ] and the depth of anesthesia [ 89 , 90 , 91 , 92 , 93 , 94 ]. Machine learning has also been applied in the fields of intensive care unit medicine [ 95 ], emergency medicine [ 96 ], and neuroimaging [ 97 ].…”

Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

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Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

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Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

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“…The ensembled classifier Random Forest (RF) integrated the final decision based on the prediction results of multiple random trees (Lu et al, 2017;Olsen et al, 2018;Rahman et al, 2018). The RandomForest algorithm is perturb-and-combine techniques specifically designed for trees.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

et al. 2020

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Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

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“…Of the selected studies, 23 conducted a retrospective analysis of the vital signs data, while 1 study [21] used a prospective cohort study design. Seventeen studies only analyzed continuous vital signs measurements collected through wearable devices and bedside monitors, whereas 3 [22][23][24] studies analyzed vital signs that were collected both manually and intermittently by clinical staff.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…While 3 studies [27][28][29] aimed to develop a remote home-based monitoring tool, the vital sign data used were obtained from the Medical Information Mart for Intensive Care (MIMIC and MIMIC-II) databases [30,31] consisting of data captured from patient monitors in different ICUs. Regarding location, 5 studies [24,26,[32][33][34] were conducted on general wards, 4 studies [11,23,35,36] were conducted in EDs, 7 studies [26,34,[37][38][39][40][41] were conducted in ICUs, 2 studies [25,42] were conducted in postoperative wards, and 4 studies [21,[43][44][45] in acute stay wards (medical admission unit, step-down units). Cohort sizes for the studies ranged from 12 patients [39] to 10,967,518 patient visits [11] (refer to Table 1).…”

Section: Study Characteristicsmentioning

confidence: 99%

“…All but one study [21] included in this review were retrospective in nature, leaving open the possibility that algorithm performance in a clinical environment may be lower than the performance achieved in a controlled retrospective setting [34]. It is also unclear how often these EWS were able to identify clinical deterioration that had not already been detected by a care team.…”

Section: Retrospective Versus Prospective Evaluationmentioning

confidence: 99%

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Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review (Preprint)

Muralitharan¹,

Nelson²,

Di³

et al. 2020

Preprint

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BACKGROUND Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively and preventing adverse outcomes. Vital signs-based aggregate-weighted Early Warning Systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. OBJECTIVE To identify, summarize, and evaluate the available research, current state of utility and challenges with machine learning based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. METHODS PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs”, “clinical deterioration”, and “machine learning”. Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. RESULTS 24 peer-reviewed studies were identified for inclusion from 417 articles. 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, ICUs, emergency departments, step-down units, medical assessment units, post-anesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. CONCLUSIONS In studies that compared performance, reported results suggest that machine learning based early warning systems can achieve greater accuracy than aggregate weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

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Towards an automated multimodal clinical decision support system at the post anesthesia care unit

Cited by 24 publications

References 13 publications

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review (Preprint)

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