A Machine Learning Based System for Analgesic Drug Delivery

González-Cava, José M.; Arnay, Rafael; Pérez, José Andrés Moreno; Leon, Ana M.; Martín, M.C.; Jove, Esteban; Calvo-Rolle, José Luís; Casteleiro‐Roca, José‐Luis; Juez, Francisco Javier de Cos

doi:10.1007/978-3-319-67180-2_45

Cited by 13 publications

(7 citation statements)

References 21 publications

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“…Nine (n = 9) papers were identified that related to the field of pain management, ranging from prediction of opioid dosing to identification of patients who may benefit from preoperative consultation with a hospital's acute pain service. 81,82 Brown et al 83 used machine learning to analyze differences in functional magnetic resonance imaging data collected from human volunteers who were exposed to painful and nonpainful thermal stimuli, demonstrating that machine learning analysis of whole brain scans could more accurately identify pain than analysis of individual brain regions traditionally associated with nociception. 83 Identification of pain was not limited to imaging techniques, Ben-Israel et al 84 developed a nociception level index that was based on machine learning analysis of photoplethysmograms and skin conductance waveforms recorded from 25 patients undergoing elective surgery.…”

Section: Artificial Intelligence In Anesthesiologymentioning

confidence: 99%

“…81,82 Brown et al 83 used machine learning to analyze differences in functional magnetic resonance imaging data collected from human volunteers who were exposed to painful and nonpainful thermal stimuli, demonstrating that machine learning analysis of whole brain scans could more accurately identify pain than analysis of individual brain regions traditionally associated with nociception. 83 Identification of pain was not limited to imaging techniques, Ben-Israel et al 84 developed a nociception level index that was based on machine learning analysis of photoplethysmograms and skin conductance waveforms recorded from 25 patients undergoing elective surgery. However, the nociception level index was based on a target described as the combined index of stimulus and analgesia that was defined and validated within the same study, including an arbitrary ranking of intraoperative noxious stimuli.…”

Section: Artificial Intelligence In Anesthesiologymentioning

confidence: 99%

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Artificial Intelligence in Anesthesiology

et al. 2020

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Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence. The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

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Section: Artificial Intelligence In Anesthesiologymentioning

confidence: 99%

Section: Artificial Intelligence In Anesthesiologymentioning

confidence: 99%

Artificial Intelligence in Anesthesiology

et al. 2020

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“…It can predict drug release profiles and drug stability (Bernick, 2015). The SVM tool has applied to the analgesic drug delivery in cholecystectomy surgeries to define an optimal administration dosage for a patient in the surgery to reduce the probable side‐effects of applying an improper dose of the drug (Gonzalez‐Cava et al, 2017).…”

Section: Application Of Deep Learning In Drug Discovery Pipelinementioning

confidence: 99%

Spectrum of deep learning algorithms in drug discovery

2020

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Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high‐throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.

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“…Es por ello que, una alternativa interesante a los laboratorios reales son los laboratorios virtuales. Hoy en día, muchos de los sistemas basados en técnicas de inteligencia artificial como los que se explican en [31,1,9,17,39,7,28,26,33,13,45,29,36,40,44,34,27,4,47,4,16,14] se pueden verificar, en las primeras fases de diseño, mediante este tipo de laboratorios.…”

Section: Introductionunclassified

Implementación virtual de prácticas de asignaturas de control como alternativa a las prácticas de laboratorio presenciales

Michelena¹,

Zayas‐Gato²,

Jove³

et al. 2021

Xlii Jornadas De Automática : Libro De Actas

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La enfermedad del coronavirus (COVID-19) ha provocado una grave crisis de salud con consecuencias socio-económicas sin precedentes. Desde la declaración de pandemia por parte de la Organización Mundial de la Salud, la mayoría de los gobiernos aplicaron políticas de control como el confinamiento, la restriccón de la movilidad, el distanciamiento social, etc. En este contexto, adaptar la planificación educativa a ese escenario conlleva un gran desafío para la comunidad educativa global. Además, dado que las prácticas presenciales de laboratorio en la educación superior fueron canceladas temporalmente, la creación de escenarios virtuales realistas mediante modernas aplicaciones software representa una alternativa interesante y necesaria. Este enfoque práctico se puede lograr mediante el uso de dos herramientas de software interesantes, como Node-RED y Factory I/O. En este trabajo se propone la implementación de una planta de laboratorio virtual utilizando las herramientas mencionadas anteriormente, con características muy similares a las plantas de laboratorio reales de la Facultad de Ingeniería de la Universidad de A Coruña.

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A Machine Learning Based System for Analgesic Drug Delivery

Cited by 13 publications

References 21 publications

Artificial Intelligence in Anesthesiology

Artificial Intelligence in Anesthesiology

Spectrum of deep learning algorithms in drug discovery

Implementación virtual de prácticas de asignaturas de control como alternativa a las prácticas de laboratorio presenciales

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