Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records

Asai, Nanaka; Doi, Chiaki; Iwai, Koki; Ideno, Satoshi; Seki, Hiroyuki; Kato, Jungo; Yamada, Takashige; Morisaki, Hiroshi; Shigeno, Hiroshi

doi:10.23919/icmu48249.2019.9006672

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…For example, Nanaka et al built a model to predict the optimal dose of anesthesia-inducing drugs by focusing on electronic anesthesia records and using a regression model, one of the machine learning methods. By adjusting the explanatory variables and parameters and using ridge regression, the determined prediction coefficient was only 0.5008, 13 so there is still a lot of room for improvement in the accuracy of anesthetic drug dose prediction. Therefore, based on previous research on anesthesia depth prediction and anesthetic drug dose prediction, this article proposes the use of deep learning methods, fully considering the timing of various physiological indicators of patients during surgery, and accurately and efficiently predicting the patient's anesthetic dose during surgery.…”

Section: Introductionmentioning

confidence: 99%

Intelligent algorithm based on deep learning to predict the dosage for anesthesia: A study on prediction of drug efficacy based on deep learning

Hu,

Pan,

Wang

et al. 2024

Health Science Reports

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Background and AimsAnesthetic drugs play a vital role during surgery, however, due to individual differences and complex physiological mechanisms, the prediction of anesthetic drug dosage has always been a challenging problem. In this study, we propose a model for predicting the dosage of anesthetic drugs based on deep learning to help anesthesiologists better control their dosage during surgical procedures.MethodsWe design a model based on the artificial neural network to predict the dosage of preoperative anesthetic, and use the SELU activation function and the loss function for weighted regularization to solve the problem of unbalanced sample. Moreover, we design a CNN‐based model for the prior extraction of intraoperative features by using a 7 × 1 convolution kernel to enhance the receptive field, and combine maximum pooling and average pooling to extract key features while eliminating noise. A predictive model based on the LSTM network is designed to predict the intraoperative dosage of the anesthetic, and the bidirectional propagation‐based LSTM network is used to improve the ability to learn the trend of changes in the physiological states of the patient during surgery. An attention module is added before the connection layer to appropriately attend to areas containing prominent features.ResultsThe results of experiments showed that the proposed method reduced values of the MAPE to 15.83% and 12.25% compared with the traditional method in predictions of the preoperative and intraoperative doses of the anesthetic, respectively, and increased the values of to 0.887 and 0.915, respectively.ConclusionThe intelligent anesthesia prediction algorithm designed in this study can effectively predict the dosage of anesthetic drugs needed by patients, assist clinical judgment of anesthetic drug dose, and assist the anesthesiologists to ensure the smooth progress of the operation.

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Section: Introductionmentioning

confidence: 99%

Intelligent algorithm based on deep learning to predict the dosage for anesthesia: A study on prediction of drug efficacy based on deep learning

Hu,

Pan,

Wang

et al. 2024

Health Science Reports

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“…Early warning of IOH events throughout the perioperative period is an effective means to prevent postoperative complications. It enables clinicians to act proactively from the usual reactive responses to hypotensive events before the consequences are seen (Davies et al 2020;Asai et al 2019).…”

Section: Introductionmentioning

confidence: 99%

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Zhou

et al. 2023

AAAI

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Intraoperative hypotension (IOH) events warning plays a crucial role in preventing postoperative complications, such as postoperative delirium and mortality. Despite significant efforts, two fundamental problems limit its wide clinical use. The well-established IOH event warning systems are often built on proprietary medical devices that may not be available in all hospitals. The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. Our framework leverages a multi-modal fusion network to make four vital signals and three demographic characteristics as input modalities. For each modality, a multi-attention mechanism is used for feature extraction for better model training. Experiments on two large-scale real-world data sets show that our method can achieve up to 94.1% accuracy on IOH events early warning while the signals sampling rate is reduced by 3000 times. Our proposal CMA can achieve a mean absolute error of 4.50 mm Hg in the most challenging 15-minute mean arterial pressure prediction task and the error reduction by 42.9% compared to existing solutions.

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Machine learning in medication prescription: A systematic review

Iancu,

Leb,

Prokosch

et al. 2023

International Journal of Medical Informatics

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Proposal of Anesthetic Dose Prediction Model to Avoid Post-induction Hypotension Using Electronic Anesthesia Records

Cited by 4 publications

References 8 publications

Intelligent algorithm based on deep learning to predict the dosage for anesthesia: A study on prediction of drug efficacy based on deep learning

Intelligent algorithm based on deep learning to predict the dosage for anesthesia: A study on prediction of drug efficacy based on deep learning

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Machine learning in medication prescription: A systematic review

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