The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients

Cherifa, Ményssa; Interian, Yannet; Blet, Alice; Resche‐Rigon, Matthieu; Pirracchio, Romain

doi:10.1016/j.artmed.2021.102118

Cited by 2 publications

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“…There is a growing interest in the use of machine learning techniques to predict the evolution of important physiologic parameters, such as the MAP in critically ill patients (4, 5, 15). However, only few studies have described the use of machine learning to predict the ICP in patients with a severe brain injury.…”

Section: Discussionmentioning

confidence: 99%

“…We have previously demonstrated that machine learning can be used to accurately predict the evolution of physiologic parameters (4, 5) using supervised ensemble machine learning methods that were proven to be superior to any single machine learning approaches in many situations (5). The goal of the present study is to use an ensemble learning approach to train and validate IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE), an ICP prediction algorithm to predict the ICP value 30 minutes in the future in patients hospitalized in the ICU with an acute brain injury and an ICP monitor.…”

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confidence: 99%

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IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

Fong,

Feng,

Hubbard

et al. 2023

Critical Care Explorations

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OBJECTIVES: Elevated intracranial pressure (ICP) is a potentially devastating complication of neurologic injury. Developing an ICP prediction algorithm to help the clinician adjust treatments and potentially prevent elevated ICP episodes. DESIGN: Retrospective study. SETTING: Three hundred thirty-five ICUs at 208 hospitals in the United States. SUBJECTS: Adults patients from the electronic ICU (eICU) Collaborative Research Database was used to train an ensemble machine learning model to predict the ICP 30 minutes in the future. Predictive performance was evaluated using a left-out test dataset and externally evaluated on the Medical Information Mart for Intensive Care-III (MIMIC-III) Matched Waveform Database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Predictors included age, assigned sex, laboratories, medications and infusions, input/output, Glasgow Coma Scale (GCS) components, and time-series vitals (heart rate, ICP, mean arterial pressure, respiratory rate, and temperature). Each patient ICU stay was divided into successive 95-minute timeblocks. For each timeblock, the model was trained on nontime-varying covariates as well as on 12 observations of time-varying covariates at 5-minute intervals and asked to predict the 5-minute median ICP 30 minutes after the last observed ICP value. Data from 931 patients with ICP monitoring in the eICU dataset were extracted (46,207 timeblocks). The root mean squared error was 4.51 mm Hg in the eICU test set and 3.56 mm Hg in the MIMIC-III dataset. The most important variables driving ICP prediction were previous ICP history, patients’ temperature, weight, serum creatinine, age, GCS, and hemodynamic parameters. CONCLUSIONS: IntraCranial pressure prediction AlgoRithm using machinE learning, an ensemble machine learning model, trained to predict the ICP of a patient 30 minutes in the future based on baseline characteristics and vitals data from the past hour showed promising predictive performance including in an external validation dataset.

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IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

Fong,

Feng,

Hubbard

et al. 2023

Critical Care Explorations

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Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Hashemi,

Yousefzadeh,

Abin

et al. 2024

J Cell Mol Anesth

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: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: "Depth of anesthesia", "Control of anesthesia delivery", "Control of mechanical ventilation and weaning", "Event prediction", "Ultrasound guidance", "Pain management", "Operating room logistic", "Monitoring", and "Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.

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The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients

Cited by 2 publications

References 35 publications

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

IntraCranial pressure prediction AlgoRithm using machinE learning (I-CARE): Training and Validation Study

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

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