Intraoperative Hypotension Prediction System by Considering Personal Lifestyle and Medical History

Inada, Riku; Doi, Chiaki; Yamasaki, Yoshio; Shigeno, Hiroshi; Seki, Hiroyuki

doi:10.1109/percomworkshops51409.2021.9430993

Cited by 2 publications

(2 citation statements)

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“…When a new IOH definition needs to be used, the adoption cost is exceedingly high as the model is required to be reconstructed again from scratch. In (Inada et al 2021),…”

Section: Introductionmentioning

confidence: 99%

“…The IOH definition is thus not fully embedded in the trained model but serves as a screening condition for event detection. This strategy creates a user-friendly interface for anesthesiologists to customize intraoperative hypotension treatment regimes based on the patient background that has been demonstrated with superior clinical effectiveness (Inada et al 2021). In summary, our contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

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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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“…When a new IOH definition needs to be used, the adoption cost is exceedingly high as the model is required to be reconstructed again from scratch. In (Inada et al 2021),…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Zhou

et al. 2023

AAAI

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Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis

Mohammadi,

Firouzabadi,

Hosseinpour

et al. 2024

J Transl Med

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Introduction Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings. Method A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI. Results 43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83). Conclusion HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.

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Intraoperative Hypotension Prediction System by Considering Personal Lifestyle and Medical History

Cited by 2 publications

References 10 publications

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis

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