Below are the most recent publications written about "Support Vector Machine" by people in Profiles.
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Pan X, Wang C, Yu Y, Reljin N, McManus DD, Darling CE, Chon KH, Mendelson Y, Lee K. Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance. Artif Intell Med. 2023 06; 140:102548.
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Bashar SK, Ding EY, Walkey AJ, McManus DD, Chon KH. Atrial Fibrillation Prediction from Critically Ill Sepsis Patients. Biosensors (Basel). 2021 Aug 09; 11(8).
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Wang L, Tang D, Maehara A, Wu Z, Yang C, Muccigrosso D, Matsumura M, Zheng J, Bach R, Billiar KL, Stone GW, Mintz GS. Using intravascular ultrasound image-based fluid-structure interaction models and machine learning methods to predict human coronary plaque vulnerability change. Comput Methods Biomech Biomed Engin. 2020 Nov; 23(15):1267-1276.
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Liu F, Pradhan R, Druhl E, Freund E, Liu W, Sauer BC, Cunningham F, Gordon AJ, Peters CB, Yu H. Learning to detect and understand drug discontinuation events from clinical narratives. J Am Med Inform Assoc. 2019 10 01; 26(10):943-951.
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Santoso LF, Baqai F, Gwozdz M, Lange J, Rosenberger MG, Sulzer J, Paydarfar D. Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound. Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul; 2019:2584-2588.
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Viswanath SE, Chirra PV, Yim MC, Rofsky NM, Purysko AS, Rosen MA, Bloch BN, Madabhushi A. Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Med Imaging. 2019 02 28; 19(1):22.
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Zhu F, Liu Y, Liu F, Yang R, Li H, Chen J, Kennedy DN, Zhao J, Guo W. Functional asymmetry of thalamocortical networks in subjects at ultra-high risk for psychosis and first-episode schizophrenia. Eur Neuropsychopharmacol. 2019 04; 29(4):519-528.
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Sakr S, Elshawi R, Ahmed A, Qureshi WT, Brawner C, Keteyian S, Blaha MJ, Al-Mallah MH. Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project. PLoS One. 2018; 13(4):e0195344.
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Reljin N, Zimmer G, Malyuta Y, Shelley K, Mendelson Y, Blehar DJ, Darling CE, Chon KH. Using support vector machines on photoplethysmographic signals to discriminate between hypovolemia and euvolemia. PLoS One. 2018; 13(3):e0195087.
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Yu H, Scalera J, Khalid M, Touret AS, Bloch N, Li B, Qureshi MM, Soto JA, Anderson SW. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom Radiol (NY). 2017 10; 42(10):2470-2478.