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

Chaudhry, Farhan; Hunt, Rachel J; Hariharan, Prashant; Anand, Sharath Kumar; Sanjay, Surya; Kjoller, E.E; Bartlett, Connor M; Johnson, Kipp W.; Levy, Phillip D.; Noushmehr, Houtan; Lee, Ian Y.

doi:10.3389/fneur.2020.554633

Cited by 19 publications

(13 citation statements)

References 52 publications

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“…As discussed in [ 161 ], congenital hydrocephalus occurs in a baby born with excess fluid in the brain; this may be caused due to spina bifida or infections from its mother and is typically progressive and should be managed effectively. SVM predicts hydrocephalus by extracting the morphological features from cranial ultrasound images [ 162 ].…”

Section: Applications Of Ai For Neurological Disordersmentioning

confidence: 99%

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

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Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

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Section: Applications Of Ai For Neurological Disordersmentioning

confidence: 99%

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…The future of critical care EEG appears promising with the improving storage capacity, and the processing power allowing for machine learning utilization. This utilization is a useful tool for predicting seizures, and for an automated interpretation of large data sets (102,103). However, these applications are not widely used in clinical practice and may not improve the workload of the electroencephalographers (104,105).…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review

2022

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Electroencephalography (EEG) is an important and relatively inexpensive tool that allows intensivists to monitor cerebral activity of critically ill patients in real time. Seizure detection in patients with and without acute brain injury is the primary reason to obtain an EEG in the Intensive Care Unit (ICU). In response to the increased demand of EEG, advances in quantitative EEG (qEEG) created an approach to review large amounts of data instantly. Finally, rapid response EEG is now available to reduce the time to detect electrographic seizures in limited-resource settings. This review article provides a concise overview of the technical aspects of EEG monitoring for seizures, clinical indications for EEG, the various available modalities of EEG, common and challenging EEG patterns, and barriers to EEG monitoring in the ICU.

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“…It is still worth exploring these from the perspective of interpretability to enable comparison. Further, it is acknowledged that there is no hard boundary between statistical inference and ML, and some methods fall into both domains [ 14 , 15 ].…”

Section: Neurointensive Care Modelsmentioning

confidence: 99%

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care

et al. 2022

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Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. Interpretable machine learning methods have the potential to provide the means to overcome some of these issues but are largely unexplored within the neurocritical care domain. This article examines existing models used in neurocritical care from the perspective of interpretability. Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.

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

Cited by 19 publications

References 52 publications

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Adult Critical Care Electroencephalography Monitoring for Seizures: A Narrative Review

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care

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