Artificial intelligence and computer simulation models in critical illness

Lal, Amos; Pinevich, Yuliya; Gajic, Ognjen; Herasevich, Vitaly; Pickering, Brian

doi:10.5492/wjccm.v9.i2.13

Cited by 28 publications

(27 citation statements)

References 40 publications

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“…The clinical utility of these models is limited by the lack of precision and underperformance in the clinical setting. In comparison, actionable AI models (or, as we have previously coined them, “Causal AI” models) are developed with explicit consideration of causal pathways, providing greater clinical utility in predicting the outcome of a given intervention as well as providing clinicians a better understanding of how the AI model is reaching its conclusions[ 17 , 18 ].…”

Section: What Is a Digital Twin?mentioning

confidence: 99%

Predictive modeling in neurocritical care using causal artificial intelligence

Dang¹,

Lal²,

Flurin³

et al. 2021

WJCCM

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Artificial intelligence (AI) and digital twin models of various systems have long been used in industry to test products quickly and efficiently. Use of digital twins in clinical medicine caught attention with the development of Archimedes, an AI model of diabetes, in 2003. More recently, AI models have been applied to the fields of cardiology, endocrinology, and undergraduate medical education. The use of digital twins and AI thus far has focused mainly on chronic disease management, their application in the field of critical care medicine remains much less explored. In neurocritical care, current AI technology focuses on interpreting electroencephalography, monitoring intracranial pressure, and prognosticating outcomes. AI models have been developed to interpret electroencephalograms by helping to annotate the tracings, detecting seizures, and identifying brain activation in unresponsive patients. In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

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Section: What Is a Digital Twin?mentioning

confidence: 99%

Predictive modeling in neurocritical care using causal artificial intelligence

Dang¹,

Lal²,

Flurin³

et al. 2021

WJCCM

Self Cite

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“…Studies have shown that transferring nurses' innovative thinking and developing abilities to clinical areas provides several benefits, including allowing for increased quality of care, earlier diagnosis of risky conditions and diseases, and additional measures to eliminate risk factors [13]. Examples of innovations used in nursing practices related to patient safety are prevention of falls, home care, medication administration, infection control, and pressure injury and wound care [11,14,32].…”

Section: The Innovative Approaches Used In the Detection And Classification Of Pressure Injuries In The World And Turkeymentioning

confidence: 99%

“…The use of mobile health applications, defined as responding to healthcare services with mobile devices, is rapidly increasing. Mobile applications are also used for a wide range of detection and classification of pressure injuries [19,[32][33][34]. Shepherd et al [35] stated that the detection, staging, and treatment of pressure injuries using information technology would be more effective.…”

Section: The Innovative Approaches Used In the Detection And Classification Of Pressure Injuries In The World And Turkeymentioning

confidence: 99%

Prediction and classification of pressure injuries by deep learning

Yilmaz¹,

Kızıl²,

Kaya³

et al. 2021

hpc

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Authors' contributionWkład autorów: A. Study design/planning zaplanowanie badań B. Data collection/entry zebranie danych C. Data analysis/statistics dane -analiza i statystyki D. Data interpretation interpretacja danych E. Preparation of manuscript przygotowanie artykułu F. Literature analysis/search wyszukiwanie i analiza literatury G. Funds collection zebranie funduszy SummaryPressure injuries are a serious medical problem that both negatively affects the patient's quality of life and results in significant healthcare costs. In cases where a patient doesn't receive appropriate treatment and care, death may result. Nurses play critical roles in the prevention, care, and treatment of pressure injuries as members of the healthcare team who closely monitor the health status of the patient. Today, the use of artificial intelligence is becoming more prevalent in healthcare, as in many other areas. Artificial intelligence is a method that aims to solve complex problems by using computers to mathematically simulate the way the brain works. In this article, we compile and share information about a deep learning model developed for the detection and classification of pressure injuries. Deep learning can operate on many types of data. Convolutional Neural Networks (CNN) prefer images because they can handle 2D arrays. In this case, the images, annotated according to the National Pressure Injury Advisory Panel pressure injury classification system, have been fed into a deep learning model using CNN. The developed CNN model has a 97% success in detecting and classifying pressure injuries, and as more images are collected and fed into the CNN, the prediction accuracy will increase. This deep learning model allows for the automatic detection and classification of pressure injuries, an indicator of health outcomes, at an early stage and for quick and accurate intervention. In this context, it is expected that the quality of nursing care will increase, the prevalence of pressure injury will decrease, and the economic burden of this health problem will decrease.

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“…To counter these flaws, we have proposed in our preliminary work a concept of developing “Causal AI” models [ 3 , 4 ]. This platform which is based on directed acyclic graphs (DAGs) developed on real life understanding of biology and pathophysiology, provides additional level of accuracy and most importantly “actionable points of intervention” which could alter the clinical course of the patient.…”

mentioning

confidence: 99%