| For: | Li XF, Huang YZ, Tang JY, Li RC, Wang XQ. Development of a random forest model for hypotension prediction after anesthesia induction for cardiac surgery. World J Clin Cases 2021; 9(29): 8729-8739 [PMID: 34734051 DOI: 10.12998/wjcc.v9.i29.8729] |
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| URL: | https://www.wjgnet.com/2307-8960/full/v9/i29/8729.htm |
| Number | Citing Articles |
| 1 |
Ali Dabbagh, A. Sassan Sabouri, Firoozeh Madadi. Artificial Intelligence in Cardiovascular and Thoracic Anesthesia. Anesthesiology Clinics 2025; 43(3): 471 doi: 10.1016/j.anclin.2025.05.003
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| 2 |
Ramakrishna Mukkamala, Michael P. Schnetz, Ashish K. Khanna, Aman Mahajan. Intraoperative Hypotension Prediction: Current Methods, Controversies, and Research Outlook. Anesthesia & Analgesia 2025; 141(1): 61 doi: 10.1213/ANE.0000000000007216
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| 3 |
Maksim Katsin, Maxim Glebov, Haim Berkenstadt, Dina Orkin, Yotam Portnoy, Adi Shuchami, Amit Yaniv-Rosenfeld, Teddy Lazebnik. Developing a machine learning-based prediction model for postinduction hypotension. Journal of Clinical Monitoring and Computing 2025; 39(5): 889 doi: 10.1007/s10877-025-01295-x
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| 4 |
Abdulsalam Mohammed Aleid, Ahmed Saeed Albashri, Jawad Saeed Albashri, Belal Ahmed Alkatheri, Faisal Saeed Albashri, Osama Abdulrahman Alraddadi, Moayyad Yousef Khojah, Turki Khalid Alsayed, Suhaib Osama Abushal, Ayman Mohammed Kharaba. Hierarchical Bayesian Particle Filtering for Artificial Intelligence-enhanced Real-time Anaesthesia Monitoring: A Dynamically Adaptive Framework. Journal of Advanced Trends in Medical Research 2025; 2(3): 608 doi: 10.4103/ATMR.ATMR_123_25
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| 5 |
Ming Chen, Dingyu Zhang. Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management. BMC Medical Informatics and Decision Making 2025; 25(1) doi: 10.1186/s12911-025-02930-y
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| 6 |
Ying Zhao, Zhe Tao, Ying Li, Huige Sun, Jingrui Tang, Qianya Wang, Liang Guo, Weiwei Song, Bailian Larry Li. Prediction of municipal solid waste generation and analysis of dominant variables in rapidly developing cities based on machine learning – a case study of China. Waste Management & Research: The Journal for a Sustainable Circular Economy 2024; 42(6): 476 doi: 10.1177/0734242X231192766
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| 7 |
Esmée C. de Boer, Joris van Houte, Catarina Dinis Fernandes, Jens Muehlsteff, R. Arthur Bouwman, Massimo Mischi. Predicting Hypotension After Spinal Anesthesia Using Carotid Ultrasound and Clinical Variables. 2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2024; : 1 doi: 10.1109/MeMeA60663.2024.10596875
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| 8 |
Pietro Arina, Maciej R. Kaczorek, Daniel A. Hofmaenner, Walter Pisciotta, Patricia Refinetti, Mervyn Singer, Evangelos B. Mazomenos, John Whittle. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140(1): 85 doi: 10.1097/ALN.0000000000004764
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| 9 |
Jelena Vučković, Milanka Tatić, Sanja Vicković, Ivana Stojanović, Katarina Mitić, Aleksandra Kontić, Lazar Velicki. Risk factors for intraoperative hypotension during cardiac surgery. Indian Journal of Thoracic and Cardiovascular Surgery 2025; 41(8): 986 doi: 10.1007/s12055-025-01961-4
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| 10 |
Romain PIRRACCHIO. The past, the present and the future of machine learning and artificial intelligence in anesthesia and Postanesthesia Care Units (PACU). Minerva Anestesiologica 2022; 88(11) doi: 10.23736/S0375-9393.22.16518-1
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| 11 |
F Gheysen, S Rex. Artificial intelligence in anesthesiology. Acta Anaesthesiologica Belgica 2023; 74(3): 185 doi: 10.56126/75.3.21
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| 12 |
Ida Mohammadi, Shahryar Rajai Firouzabadi, Melika Hosseinpour, Mohammadhosein Akhlaghpasand, Bardia Hajikarimloo, Roozbeh Tavanaei, Amirreza Izadi, Sam Zeraatian-Nejad, Foolad Eghbali. Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis. Journal of Translational Medicine 2024; 22(1) doi: 10.1186/s12967-024-05481-4
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| 13 |
Maya Patel, Karen C. Nanji. Artificial Intelligence in Perioperative Medication-Related Clinical Decision Support. Anesthesiology Clinics 2025; 43(3): 587 doi: 10.1016/j.anclin.2025.05.009
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| 14 |
Danila Azzolina, Gianmaria Cammarota, Enrico Boero, Paola Berchialla, Savino Spadaro, Federico Longhini, Cristian Deana, Daniele Guerino Biasucci, Stefano D’Incà, Irene Batticci, Nicola Fasano, Edoardo De Robertis, Rachele Simonte, Salvatore Maurizio Maggiore, Valentina Bellini, Elena Giovanna Bignami, Luigi Vetrugno, Vito Marco Ranieri, Anna Pesamosca, Agnese Cattarossi, Saskia Granzotti, Alessandro Cavarape, Andrea Cortegiani, Lisa Mattuzzi, Luca Flaibani, Nicola Federici, Francesco Meroi, Marco Tescione, Andrea Bruni, Eugenio Garofalo, Mattia Bernardinetti, Felice Urso, Camilla Colombotto, Francesco Forfori, Sandro Pregnolato, Francesco Corradi, Federico Dazzi, Sara Tempini, Alessandro Isirdi, Moro Federico, Nicole Giovane, Milo Vason, Carlo Alberto Volta, Fabio Gori, Michela Neri, Auro Caraffa, Giovanni Cosco, Eugenio Vadalà, Demetrio Labate, Nicola Polimeni, Marilena Napolitano, Sebastiano Macheda, Angela Corea, Lucia Lentin, Michele Divella, Daniele Orso, Clara Zaghis, Silvia Del Rio, Serena Tomasino, Alessandro Brussa, Natascia D’Andrea, Simone Bressan, Giuseppe Neri, Pietro Giammanco, Alberto Nicolò Galvano, Mariachiara Ippolito, Fabrizio Ricci, Francesca Stefani, Lolita Fasoli, Piergiorgio Bresil, Federica Curto, Lorenzo Pirazzoli, Carlo Frangioni, Mattia Puppo, Sabrina Mussetta, Michele Autelli, Giuseppe Giglio, Filippo Riccone, Erika Taddei. An interpretable machine learning tool for predicting perioperative cardiac events in patients scheduled for hip fracture surgery: insights from the multicenter LUSHIP study. Journal of Anesthesia, Analgesia and Critical Care 2025; 5(1) doi: 10.1186/s44158-025-00291-6
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| 15 |
Shinichi Tamaru, Hirotsugu Suwanai, Hironori Abe, Junko Sasaki, Keitaro Ishii, Hajime Iwasaki, Jumpei Shikuma, Rokuro Ito, Takashi Miwa, Toru Sasaki, Tomoko Takamiya, Shigeru Inoue, Kazuhiro Saito, Masato Odawara, Ryo Suzuki. Machine learning approach to predict subtypes of primary aldosteronism is helpful to estimate indication of adrenal vein sampling. High Blood Pressure & Cardiovascular Prevention 2022; 29(4): 375 doi: 10.1007/s40292-022-00523-8
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| 16 |
Guangshan Jin, Fuqiang Liu, Yiwen Yang, Jiahui Chen, Qian Wen, Yudong Wang, Ling Yu, Jianhua He. Carotid blood flow changes following a simulated end-inspiratory occlusion maneuver measured by ultrasound can predict hypotension after the induction of general anesthesia: an observational study. BMC Anesthesiology 2024; 24(1) doi: 10.1186/s12871-023-02393-6
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| 17 |
Sana Hashemi, Zohreh Yousefzadeh, Ahmad Ali Abin, Azar Ejmalian, Shahabedin Nabavi, Ali Dabbagh. Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications. Journal of Cellular & Molecular Anesthesia 2024; 9(1) doi: 10.5812/jcma-145369
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