A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Lu, Feng; Li, Wei; Zhou, Zhiqiang; Song, Cheng; Sun, Yahui; Zhang, Yuwei; Ren, Yufei; Liao, Xiaofei; Jin, Hai; Luo, Ailin; Zomaya, Albert Y.

doi:10.1609/aaai.v37i12.26681

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Explanation methods are designed to investigate the factors behind a decision and expose any biases or unintended effects of AI models. The transparency and explainability provided by these methods increase human trust in AI models, allowing for more extensive and profound use of AI (Kanamori et al 2020;Albini et al 2020;Lu et al 2023b).…”

Section: Introductionmentioning

confidence: 99%

CGS-Mask: Making Time Series Predictions Intuitive for All

Lu,

Li,

Sun

et al. 2024

AAAI

View full text Add to dashboard Cite

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease.

show abstract

Section: Introductionmentioning

confidence: 99%

CGS-Mask: Making Time Series Predictions Intuitive for All

Lu,

Li,

Sun

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

A Composite Multi-Attention Framework for Intraoperative Hypotension Early Warning

Cited by 2 publications

References 15 publications

CGS-Mask: Making Time Series Predictions Intuitive for All

CGS-Mask: Making Time Series Predictions Intuitive for All

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

Contact Info

Product

Resources

About