Machine learning-based dynamic mortality prediction after traumatic brain injury

Raj, Rahul; Luostarinen, Teemu; Pursiainen, Eetu; Posti, Jussi P.; Takala, Riikka; Bendel, Stepani; Konttila, Teijo; Korja, Miikka

doi:10.1038/s41598-019-53889-6

Cited by 91 publications

(81 citation statements)

References 39 publications

(37 reference statements)

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“…Raj et al ( 27 ) developed a dynamic multiple regression prediction model to predict 30-day all-cause mortality after traumatic brain injury (TBI). The model used variables gathered from continuous ICP waveform measurements from intraventricular catheter or an intraparenchymal probe and other physiological measurements (i.e., mean arterial blood pressure).…”

Section: Machine Learning Improves Monitoring With Intracranial Pressmentioning

confidence: 99%

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

et al. 2020

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The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.

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Section: Machine Learning Improves Monitoring With Intracranial Pressmentioning

confidence: 99%

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

et al. 2020

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“…ANNs have been used to predict outcome following head injury. 42,43 The occurrence of symptomatic cerebral vasospasm (SCV) after aneurysmal subarachnoid hemorrhage (aSAH) is a morbid and common problem. A simple ANN model was found to be more sensitive and specific than multiple logistic regression (MLR) models in prediction of SCV in patients with aSAH.…”

Section: Predictive Analyticsmentioning

confidence: 99%

Artificial Intelligence in Neuroanesthesiology and Neurocritical Care

Rajagopalan

Kulkarni²

2020

Journal of Neuroanaesthesiology and Critical Care

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Artificial intelligence (AI) already influences almost every sector of our daily life, including the rapidly evolving technologies and datasets of healthcare delivery. The applications in medicine have significantly evolved over the past few decades and have shown promising results. Despite constant efforts to incorporate AI into the field of anesthesiology since its inception, it is still not commonplace. Neuroanesthesiology and neurocritical care is a discipline of medicine that deals with patients having disorders of the nervous system comprising a complex combination of both medical and surgical disease conditions. AI can be used for better monitoring, treatment, and outcome prediction, thereby reducing healthcare costs, minimizing delays in patient management, and avoiding medical errors. In this review, we have discussed the applications of AI and its potential in aiding the clinician’s judgment in several aspects of neuroanesthesiology and neurocritical care, some of the barriers to its implementation, and the future trends in improving education in this field, all of which will require further work to understand its exact scope.

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“…The application of arti cial intelligence and machine learning algorithms may further improve the predictive accuracy. Rahul et al created a machine learning-based algorithm that could dynamically predict mortality in critically traumatic brain injury [47]. Therefore, future study should focus on constructing a machine learning-based algorithm based on combine multiple indicators.…”

Section: Further Study Implicationsmentioning

confidence: 99%

Change in age distribution of the injuries and comparison of different scoring tools in predicting mortality among geriatric trauma patients

Jiang

Zheng

Zhang

2020

Preprint

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Purpose: The aim of this study was to describe the age change tendency of trauma patients and to test the accuracy of different scoring tools in prediction of in-hospital mortality in case of geriatric trauma.Methods: Annual reports released by the National Trauma Database (NTDB) in the United States from 2005 to 2015 and the Trauma Register DGU® in German from 1994 to 2012 were used to describe the age change tendency of trauma patients. Secondary analysis of a single-center cohort study conducted among 311 severely injured geriatric trauma patients in a level Ⅰ trauma center in Switzerland was completed. According to the in-hospital survival status, patients were divided into survival group and non-survival group. Receiver Operating Characteristic Curve (ROC) analysis was used to evaluated the predictive performance of the ISS (injury severity score); NISS (new injury severity score), APACHE Ⅱ (Acute Physiology and Chronic Health Evaluation Ⅱ), SPAS Ⅱ (simplified acute physiology score Ⅱ) and TRISS (Trauma and Injury Severity Score) in prediction of in-hospital mortality among geriatric trauma patients. Results: The analysis of the NTDB showed the proportion of geriatric trauma increased from 18% to 30% from 2005 to 2015. The analysis of the DGU® showed the mean age of trauma patients rose from 39.11 in 1993 to 51.10 in 2013, and the proportion of patients aged ≥60 rose from 16.5% to 37.5%. The secondary analysis indicated one hundred and sixty-four (52.73%) patients died in the hospital. The ISS, NISS, APACHE Ⅱ, and SAPS Ⅱ in the death group were significantly higher than those in the survival group, and the TRISS in the death group was significantly lower than those in the survival group. The AUC of the ISS, NISS, TRISS, APACHE Ⅱ, and SAPS Ⅱ was 0.807, 0.850, 0.828, 0.715 and 0.725, respectively.Conclusion: The total number of geriatric trauma is increasing as the population ages. The accuracy of ISS, NISS and TRISS was higher than the accuracy of the APACHE Ⅱ and SAPS Ⅱ to predict in-hospital mortality in case of geriatric trauma.

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Machine learning-based dynamic mortality prediction after traumatic brain injury

Cited by 91 publications

References 39 publications

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

Artificial Intelligence in Neuroanesthesiology and Neurocritical Care

Change in age distribution of the injuries and comparison of different scoring tools in predicting mortality among geriatric trauma patients

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