Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model

Hwang, Eugene; Park, Yong-Seok; Kim, Jin‐Young; Park, Sung-Hyuk; Kim, Junetae; Kim, Sung-Hoon

doi:10.1109/tnnls.2023.3273187

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…In this study, PCA was performed for different numbers of principal components (3,4,5,6,7,8,9,10). The goal was to assess whether generating artificial features through PCA would enhance the performance of the model.…”

Section: Principal Component Analysismentioning

confidence: 99%

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Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

Kurek,

Niedbała,

Wojciechowski

et al. 2023

Agriculture

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This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.

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Section: Principal Component Analysismentioning

confidence: 99%

“…Hybrid forms of sensor systems and crop growth models will provide better information on crop growth during the season. This, combined with weighing systems and cameras on harvesters, will provide site-specific information on the yield and quality of harvested potatoes [7,8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

Kurek,

Niedbała,

Wojciechowski

et al. 2023

Agriculture

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Forecasting intraoperative hypotension during hepatobiliary surgery

Cata,

Soni,

Bhavsar

et al. 2024

J Clin Monit Comput

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The applications and prospects of big data in perioperative anesthetic management

Zhu,

Liu,

et al. 2024

APS

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Perioperative anesthetic management entails a multitude of decision-making processes within complex medical scenarios. These demand the continuous and dynamic execution of precise decisions which poses significant challenges. In the age of big data, the exponential growth in data volume from diverse sources has revolutionized many fields, including healthcare, finance, and marketing. Machine learning has emerged as a powerful tool for analyzing big data, enabling the handling of large datasets and uncovering intricate patterns and relationships. The application of big data and artificial intelligence algorithms is gradually being integrated, enabling effective task completion in various stages of perioperative management, including risk prediction, decision support, and auxiliary examination. Through in-depth analysis of big data, healthcare professionals can gain insights into patient prognoses. This review provides a comprehensive overview of the distinctive features of perioperative big data and its applications in anesthesia management during the perioperative period.

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Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model

Cited by 3 publications

References 54 publications

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

Forecasting intraoperative hypotension during hepatobiliary surgery

The applications and prospects of big data in perioperative anesthetic management

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